2000 character limit reached
Ensuring connectedness for the Maximum Quasi-clique and Densest $k$-subgraph problems (2403.08534v1)
Published 13 Mar 2024 in cs.DM
Abstract: Given an undirected graph $G$, a quasi-clique is a subgraph of $G$ whose density is at least $\gamma$ $(0 < \gamma \leq 1)$. Two optimization problems can be defined for quasi-cliques: the Maximum Quasi-Clique (MQC) Problem, which finds a quasi-clique with maximum vertex cardinality, and the Densest $k$-Subgraph (DKS) Problem, which finds the densest subgraph given a fixed cardinality constraint. Most existing approaches to solve both problems often disregard the requirement of connectedness, which may lead to solutions containing isolated components that are meaningless for many real-life applications. To address this issue, we propose two flow-based connectedness constraints to be integrated into known Mixed-Integer Linear Programming (MILP) formulations for either MQC or DKS problems. We compare the performance of MILP formulations enhanced with our connectedness constraints in terms of both running time and number of solved instances against existing approaches that ensure quasi-clique connectedness. Experimental results demonstrate that our constraints are quite competitive, making them valuable for practical applications requiring connectedness.
- Brunato, M., Hoos, H.H., Battiti, R.: On effectively finding maximal quasi-cliques in graphs. In: Maniezzo, V., Battiti, R., Watson, J.-P. (eds.) Learning and Intelligent Optimization, pp. 41–55. Springer, Berlin, Heidelberg (2008). https://doi.org/10.1007/978-3-540-92695-5_4 Pattillo et al. [2012] Pattillo, J., Youssef, N., Butenko, S.: Clique relaxation models in social network analysis. In: Thai, M.T., Pardalos, P.M. (eds.) Handbook of Optimization in Complex Networks: Communication and Social Networks, pp. 143–162. Springer, New York, NY (2012). https://doi.org/10.1007/978-1-4614-0857-4_5 Abello et al. [1999] Abello, J., Pardalos, P.M., Resende, M.G.C.: On Maximum Clique Problems in Very Large Graphs, pp. 119–130. American Mathematical Society, USA (1999). https://doi.org/10.1090/dimacs/050 Asahiro et al. [2000] Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Youssef, N., Butenko, S.: Clique relaxation models in social network analysis. In: Thai, M.T., Pardalos, P.M. (eds.) Handbook of Optimization in Complex Networks: Communication and Social Networks, pp. 143–162. Springer, New York, NY (2012). https://doi.org/10.1007/978-1-4614-0857-4_5 Abello et al. [1999] Abello, J., Pardalos, P.M., Resende, M.G.C.: On Maximum Clique Problems in Very Large Graphs, pp. 119–130. American Mathematical Society, USA (1999). https://doi.org/10.1090/dimacs/050 Asahiro et al. [2000] Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Pardalos, P.M., Resende, M.G.C.: On Maximum Clique Problems in Very Large Graphs, pp. 119–130. American Mathematical Society, USA (1999). https://doi.org/10.1090/dimacs/050 Asahiro et al. [2000] Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Pardalos, P.M., Resende, M.G.C.: On Maximum Clique Problems in Very Large Graphs, pp. 119–130. American Mathematical Society, USA (1999). https://doi.org/10.1090/dimacs/050 Asahiro et al. [2000] Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K., Tamaki, H., Tokuyama, T.: Greedily finding a dense subgraph. Journal of Algorithms 34(2), 203–221 (2000) https://doi.org/10.1006/jagm.1999.1062 Chang et al. [2014] Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. 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Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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(eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chang, M.-S., Chen, L.-H., Hung, L.-J., Rossmanith, P., Wu, G.-H.: Exact algorithms for problems related to the densest k-set problem. Information Processing Letters 114(9), 510–513 (2014) https://doi.org/10.1016/j.ipl.2014.04.009 Corneil and Perl [1984] Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. 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Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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(eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. 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In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Corneil, D.G., Perl, Y.: Clustering and domination in perfect graphs. Discrete Applied Mathematics 9(1), 27–39 (1984) https://doi.org/10.1016/0166-218X(84)90088-X Kortsarz and Peleg [1993] Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. 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European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. 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[2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Kortsarz, G., Peleg, D.: On choosing a dense subgraph. In: Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science, pp. 692–701 (1993). https://doi.org/10.1109/SFCS.1993.366818 Feige and Seltser [1997] Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Seltser, M.: On the densest k-subgraph problems. Technical Report CS97-16, Weizmann Institute, Department of Applied Math and Computer Science, Rehovot, Israel (1997) Chen et al. [2017] Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. 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[2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. 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[2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Chen, X., Hu, X., Wang, C.: Finding connected k-subgraphs with high density. Information and Computation 256, 160–173 (2017) https://doi.org/10.1016/j.ic.2017.07.003 Asahiro and Iwama [1995] Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Asahiro, Y., Iwama, K.: Finding dense subgraphs. In: Staples, J., Eades, P., Katoh, N., Moffat, A. (eds.) Algorithms and Computations, pp. 102–111. Springer, Berlin, Heidelberg (1995). https://doi.org/10.1007/BFb0015413 Pattillo et al. [2013] Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pattillo, J., Veremyev, A., Butenko, S., Boginski, V.: On the maximum quasi-clique problem. Discrete Applied Mathematics 161(1), 244–257 (2013) https://doi.org/10.1016/j.dam.2012.07.019 Abello et al. [2002] Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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(eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002: Theoretical Informatics, pp. 598–612. Springer, Berlin, Heidelberg (2002). https://doi.org/10.1007/3-540-45995-2_51 Althaus et al. [2014] Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. 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[2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Althaus, E., Blumenstock, M., Disterhoft, A., Hildebrandt, A., Krupp, M.: Algorithms for the maximum weight connected k-induced subgraph problem. In: Zhang, Z., Wu, L., Xu, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications, pp. 268–282. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12691-3_21 Backes et al. [2011] Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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[2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. 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In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Backes, C., Rurainski, A., Klau, G.W., Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., Lenhof, H.-P.: An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Nucleic Acids Research 40(6), 43–43 (2011) https://doi.org/10.1093/nar/gkr1227 Bhattacharyya and Bandyopadhyay [2009] Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. 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European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhattacharyya, M., Bandyopadhyay, S.: Mining the largest quasi-clique in human protein interactome. In: 2009 International Conference on Adaptive and Intelligent Systems, pp. 194–199 (2009). https://doi.org/10.1109/ICAIS.2009.39 Billionnet [2005] Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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[2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. 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[2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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[2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. 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[2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Billionnet, A.: Different formulations for solving the heaviest k-subgraph problem. INFOR: Information Systems and Operational Research 43(3), 171–186 (2005) https://doi.org/10.1080/03155986.2005.11732724 Bourgeois et al. [2013] Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bourgeois, N., Giannakos, A., Lucarelli, G., Milis, I., Paschos, V.T.: Exact and approximation algorithms for densest k-subgraph. In: Ghosh, S.K., Tokuyama, T. (eds.) WALCOM: Algorithms and Computation, pp. 114–125. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36065-7_12 Marinelli et al. [2021] Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Marinelli, F., Pizzuti, A., Rossi, F.: Lp-based dual bounds for the maximum quasi-clique problem. Discrete Applied Mathematics 296, 118–140 (2021) https://doi.org/10.1016/j.dam.2020.02.003 . 16th Cologne–Twente Workshop on Graphs and Combinatorial Optimization (CTW 2018) Pajouh et al. [2014] Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. 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In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. 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[2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Pajouh, F.M., Miao, Z., Balasundaram, B.: A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research 216(1), 145–161 (2014) https://doi.org/10.1007/s10479-012-1242-y Ribeiro and Riveaux [2019] Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. 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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. 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In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Ribeiro, C.C., Riveaux, J.A.: An exact algorithm for the maximum quasi-clique problem. International Transactions in Operational Research 26(6), 2199–2229 (2019) https://doi.org/10.1111/itor.12637 Veremyev et al. [2016] Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Veremyev, A., Prokopyev, O.A., Butenko, S., Pasiliao, E.L.: Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs. Computational Optimization and Applications 64(1), 177–214 (2016) https://doi.org/10.1007/s10589-015-9804-y Chen et al. [2021] Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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[2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. 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Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Chen, J., Cai, S., Pan, S., Wang, Y., Lin, Q., Zhao, M., Yin, M.: NuQClq: An effective local search algorithm for maximum quasi-clique problem. Proceedings of the AAAI Conference on Artificial Intelligence 35(14), 12258–12266 (2021) https://doi.org/10.1609/aaai.v35i14.17455 Djeddi et al. [2019] Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Djeddi, Y., Haddadene, H.A., Belacel, N.: An extension of adaptive multi-start tabu search for the maximum quasi-clique problem. Computers & Industrial Engineering 132, 280–292 (2019) https://doi.org/10.1016/j.cie.2019.04.040 Macambira [2002] Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. 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Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Macambira, E.M.: An application of tabu search heuristic for the maximum edge-weighted subgraph problem. Annals of Operations Research 117(1-4), 175–190 (2002) https://doi.org/10.1023/A:1021525624027 Peng et al. [2021] Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Peng, B., Wu, L., Wang, Y., Wu, Q.: Solving maximum quasi-clique problem by a hybrid artificial bee colony approach. Information Sciences 578, 214–235 (2021) https://doi.org/10.1016/j.ins.2021.06.094 Pinto et al. [2021] Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Pinto, B.Q., Ribeiro, C.C., Riveaux, J.A., Rosseti, I.: A brkga-based matheuristic for the maximum quasi-clique problem with an exact local search strategy. RAIRO-Operations Research 55, 741–763 (2021) https://doi.org/10.1051/ro/2020003 Pinto et al. [2018] Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. 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Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? 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[2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 271(3), 849–865 (2018) https://doi.org/10.1016/j.ejor.2018.05.071 Zhou et al. [2020] Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. 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[2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Zhou, Q., Benlic, U., Wu, Q.: An opposition-based memetic algorithm for the maximum quasi-clique problem. European Journal of Operational Research 286(1), 63–83 (2020) https://doi.org/10.1016/j.ejor.2020.03.019 Bhaskara et al. [2010] Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. 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Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. 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(2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. 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- Bhaskara, A., Charikar, M., Chlamtac, E., Feige, U., Vijayaraghavan, A.: Detecting high log-densities: An O(n1/4)superscript𝑛14(n^{1/4})( italic_n start_POSTSUPERSCRIPT 1 / 4 end_POSTSUPERSCRIPT ) approximation for densest k-subgraph. In: Proceedings of the Forty-Second ACM Symposium on Theory of Computing. STOC ’10, pp. 201–210. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1806689.1806719 Feige et al. [2001] Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. 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In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Feige, U., Kortsarz, G., Peleg, D.: The dense k-subgraph problem. Algorithmica 29(3), 410–421 (2001) https://doi.org/10.1007/s004530010050 Hochbaum and Pathria [1994] Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Hochbaum, D.S., Pathria, A.: Node-optimal connected k-subgraphs. Manuscript, UC Berkeley (1994) Liazi et al. [2008] Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Liazi, M., Milis, I., Zissimopoulos, V.: A constant approximation algorithm for the densest k-subgraph problem on chordal graphs. Information Processing Letters 108(1), 29–32 (2008) https://doi.org/10.1016/j.ipl.2008.03.016 Fortunato [2010] Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010) https://doi.org/10.1016/j.physrep.2009.11.002 Gschwind et al. [2015] Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Gschwind, T., Irnich, S., Furini, F., Calvo, R.W.: Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques. Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz (December 2015). https://ideas.repec.org/p/jgu/wpaper/1520.html Komusiewicz et al. [2015] Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Komusiewicz, C., Sorge, M., Stahl, K.: Finding connected subgraphs of fixed minimum density: Implementation and experiments. In: Bampis, E. (ed.) Experimental Algorithms, pp. 82–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20086-6_7 Komusiewicz and Sommer [2020] Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Komusiewicz, C., Sommer, F.: Fixcon: A generic solver for fixed-cardinality subgraph problems. In: Blelloch, G.E., Finocchi, I. (eds.) Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, pp. 12–26. SIAM, ??? (2020). https://doi.org/10.1137/1.9781611976007.2 Gavish [1982] Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Gavish, B.: Topological design of centralized computer networks—formulations and algorithms. Networks 12(4), 355–377 (1982) https://doi.org/10.1002/net.3230120402 Dilkina and Gomes [2010] Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Dilkina, B., Gomes, C.P.: Solving connected subgraph problems in wildlife conservation. In: Lodi, A., Milano, M., Toth, P. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 102–116. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_14 Davis and Hu [2011] Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663 Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663
- Davis, T.A., Hu, Y.: The university of florida sparse matrix collection. ACM Transactions on Mathematical Software 38(1) (2011) https://doi.org/10.1145/2049662.2049663