Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Faster Branching Algorithm for the Maximum $k$-Defective Clique Problem (2407.16588v2)

Published 23 Jul 2024 in cs.DS and cs.AI

Abstract: A $k$-defective clique of an undirected graph $G$ is a subset of its vertices that induces a nearly complete graph with a maximum of $k$ missing edges. The maximum $k$-defective clique problem, which asks for the largest $k$-defective clique from the given graph, is important in many applications, such as social and biological network analysis. In the paper, we propose a new branching algorithm that takes advantage of the structural properties of the $k$-defective clique and uses the efficient maximum clique algorithm as a subroutine. As a result, the algorithm has a better asymptotic running time than the existing ones. We also investigate upper-bounding techniques and propose a new upper bound utilizing the \textit{conflict relationship} between vertex pairs. Because conflict relationship is common in many graph problems, we believe that this technique can be potentially generalized. Finally, experiments show that our algorithm outperforms state-of-the-art solvers on a wide range of open benchmarks.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (31)
  1. Community detection in large-scale social networks: state-of-the-art and future directions. Social Network Analysis and Mining, 9(1):23, 2019.
  2. P. Bedi and C. Sharma. Community detection in social networks. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 6(3):115–135, 2016.
  3. R. Behar and S. Cohen. Finding all maximal connected s-cliques in social networks. In EDBT, pages 61–72, 2018.
  4. A branch-and-bound algorithm for the knapsack problem with conflict graph. INFORMS Journal on Computing, 29(3):457–473, 2017.
  5. Statistical analysis of financial networks. Computational statistics & data analysis, 48(2):431–443, 2005.
  6. Mining market data: A network approach. Computers & Operations Research, 33(11):3171–3184, 2006.
  7. L. Chang. Efficient maximum clique computation over large sparse graphs. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 529–538, 2019.
  8. L. Chang. Efficient maximum k-defective clique computation with improved time complexity. Proceedings of the ACM on Management of Data, 1(3):1–26, 2023.
  9. L. Chang. Maximum defective clique computation: Improved time complexities and practical performance. arXiv preprint arXiv:2403.07561, 2024.
  10. Computing maximum k-defective cliques in massive graphs. Computers & Operations Research, 127:105131, 2021.
  11. D2k: Scalable community detection in massive networks via small-diameter k-plexes. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1272–1281. ACM, 2018.
  12. Maximal defective clique enumeration. Proceedings of the ACM on Management of Data, 1(1):1–26, 2023.
  13. Community detection in large-scale social networks. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 16–25, 2007.
  14. An exact algorithm with new upper bounds for the maximum k-defective clique problem in massive sparse graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 10174–10183, 2022.
  15. Maximum weight relaxed cliques and russian doll search revisited. Discrete Applied Mathematics, 234:131–138, 2018.
  16. Uniform integration of genome mapping data using intersection graphs. Bioinformatics, 17(6):487–494, 2001.
  17. J. Håstad. Clique is hard to approximate withinn 1- ε𝜀\varepsilonitalic_ε. Acta Mathematica, 182(1):105–142, 1999.
  18. Kd-club: An efficient exact algorithm with new coloring-based upper bound for the maximum k-defective clique problem. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 20735–20742, 2024.
  19. S. Khot and V. Raman. Parameterized complexity of finding subgraphs with hereditary properties. Theoretical Computer Science, 289(2):997–1008, 2002.
  20. On minimization of the number of branches in branch-and-bound algorithms for the maximum clique problem. Computers & Operations Research, 84:1–15, 2017.
  21. Uncovering the largest community in social networks at scale. In IJCAI, pages 2251–2260, 2023.
  22. Smallest-last ordering and clustering and graph coloring algorithms. Journal of the ACM (JACM), 30(3):417–427, 1983.
  23. The network data repository with interactive graph analytics and visualization. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. URL http://networkrepository.com.
  24. Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations. Computational Optimization and Applications, 56(1):113–130, 2013.
  25. Listing maximal k-plexes in large real-world graphs. In Proceedings of the ACM Web Conference 2022, pages 1517–1527, 2022.
  26. A fast maximum k-plex algorithm parameterized by the degeneracy gap. In E. Elkind, editor, Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, pages 5648–5656. International Joint Conferences on Artificial Intelligence Organization, 8 2023. 10.24963/ijcai.2023/627. URL https://doi.org/10.24963/ijcai.2023/627. Main Track.
  27. N. Wünsche. Mind the gap when searching for relaxed cliques. Master’s thesis, Technische Universität Berlin, 2021.
  28. M. Xiao and H. Nagamochi. Exact algorithms for maximum independent set. Information and Computation, 255:126–146, 2017.
  29. M. Yannakakis. Node-and edge-deletion NP-complete problems. In Proceedings of the 10th Annual ACM Symposium on Theory of Computing, pages 253–264. ACM, 1978.
  30. Predicting interactions in protein networks by completing defective cliques. Bioinformatics, 22(7):823–829, 2006.
  31. Improving maximum k-plex solver via second-order reduction and graph color bounding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12453–12460, 2021.

Summary

We haven't generated a summary for this paper yet.