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A Starting Point for Dynamic Community Detection with Leiden Algorithm (2405.11658v4)

Published 19 May 2024 in cs.DC and cs.SI

Abstract: Real-world graphs often evolve over time, making community or cluster detection a crucial task. In this technical report, we extend three dynamic approaches - Naive-dynamic (ND), Delta-screening (DS), and Dynamic Frontier (DF) - to our multicore implementation of the Leiden algorithm, known for its high-quality community detection. Our experiments, conducted on a server with a 64-core AMD EPYC-7742 processor, show that ND, DS, and DF Leiden achieve average speedups of 1.37x, 1.47x, and 1.98x on large graphs with random batch updates, compared to the Static Leiden algorithm - while scaling at a rate of 1.6x for every doubling of threads. To our knowledge, this is the first attempt to apply dynamic approaches to the Leiden algorithm. We hope these early results pave the way for further development of dynamic approaches for evolving graphs.

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References (49)
  1. A dynamic modularity based community detection algorithm for large-scale networks: DSLM. In Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining. 1177–1183.
  2. T. Aynaud and J. Guillaume. 2010. Static community detection algorithms for evolving networks. In 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. IEEE, IEEE, Avignon, France, 513–519.
  3. Dynamic algorithms for graph coloring. In Proc. of 29th ACM-SIAM SODA. 1–20.
  4. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (Oct 2008), P10008.
  5. On modularity clustering. IEEE transactions on knowledge and data engineering 20, 2 (2007), 172–188.
  6. W. Chong and L. Teow. 2013. An incremental batch technique for community detection. In Proceedings of the 16th International Conference on Information Fusion. IEEE, IEEE, Istanbul, Turkey, 750–757.
  7. Dynamic community detection in evolving networks using locality modularity optimization. Social Network Analysis and Mining 6, 1 (2016), 1–20.
  8. E. Côme and P. Latouche. 2015. Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood. Statistical Modelling 15 (3 2015), 564–589. Issue 6. https://doi.org/10.1177/1471082X15577017 doi: 10.1177/1471082X15577017.
  9. Clique percolation in random networks. Physical review letters 94, 16 (2005), 160202.
  10. S. Fortunato. 2010. Community detection in graphs. Physics reports 486, 3-5 (2010), 75–174.
  11. A fast parallel genetic algorithm based approach for community detection in large networks. In 11th International Conference on Communication Systems & Networks (COMSNETS). IEEE, Bangalore, India, 95–101.
  12. S. Gregory. 2010. Finding overlapping communities in networks by label propagation. New Journal of Physics 12 (10 2010), 103018. Issue 10.
  13. R. Guimera and L. Amaral. 2005. Functional cartography of complex metabolic networks. nature 433, 7028 (2005), 895–900.
  14. A review of clique-based overlapping community detection algorithms. Knowledge and Information Systems 64, 8 (2022), 2023––2058.
  15. Dynamic clustering in social networks using louvain and infomap method. In Third European Network Intelligence Conference (ENIC). IEEE, IEEE, Wroclaw, Poland, 61–68.
  16. A parallel algorithm template for updating single-source shortest paths in large-scale dynamic networks. IEEE TPDS 33, 4 (2021), 929–940.
  17. K. Kloster and D. Gleich. 2014. Heat kernel based community detection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, USA, 1386–1395.
  18. The SuiteSparse matrix collection website interface. JOSS 4, 35 (Mar 2019), 1244.
  19. A. Lancichinetti and S. Fortunato. 2009. Community detection algorithms: a comparative analysis. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 80, 5 Pt 2 (Nov 2009), 056117.
  20. J. Leskovec. 2021. CS224W: Machine Learning with Graphs — 2021 — Lecture 13.3 - Louvain Algorithm. https://www.youtube.com/watch?v=0zuiLBOIcsw
  21. Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.
  22. Y. Lu and G. Chakraborty. 2020. Improving Efficiency of Graph Clustering by Genetic Algorithm Using Multi-Objective Optimization. International Journal of Applied Science and Engineering 17, 2 (Jun 2020), 157–173.
  23. A novel dynamic community detection algorithm based on modularity optimization. In 7th IEEE international conference on software engineering and service science (ICSESS). IEEE, IEEE, Beijing,China, 72–75.
  24. M. Newman. 2004. Detecting community structure in networks. The European Physical Journal B - Condensed Matter 38, 2 (Mar 2004), 321–330.
  25. M. Newman. 2006. Finding community structure in networks using the eigenvectors of matrices. Physical review E 74, 3 (2006), 036104.
  26. M. Newman and G. Reinert. 2016. Estimating the number of communities in a network. Physical review letters 117, 7 (2016), 078301.
  27. OpenMP Architecture Review Board. 2018. OpenMP Application Program Interface Version 5.0. https://www.openmp.org/wp-content/uploads/OpenMP-API-Specification-5.0.pdf
  28. A simple acceleration method for the Louvain algorithm. International Journal of Computer and Electrical Engineering 8, 3 (2016), 207.
  29. Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76, 3 (Sep 2007), 036106–1–036106–11.
  30. Efficient parallel algorithms for dynamic closeness-and betweenness centrality. In Proc. ACM ICS. e6650:1–12.
  31. J. Reichardt and S. Bornholdt. 2006. Statistical mechanics of community detection. Physical review E 74, 1 (2006), 016110.
  32. L. Rita. 2020. Infomap Algorithm. An algorithm for community finding. https://towardsdatascience.com/infomap-algorithm-9b68b7e8b86
  33. The map equation. The European Physical Journal Special Topics 178, 1 (Nov 2009), 13–23.
  34. M. Rosvall and C. Bergstrom. 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences 105, 4 (2008), 1118–1123.
  35. Community discovery: simple and scalable approaches. Springer International Publishing, Cham, 23–54.
  36. S. Ryu and D. Kim. 2016. Quick community detection of big graph data using modified louvain algorithm. In IEEE 18th International Conference on High Performance Computing and Communications (HPCC). IEEE, Sydney, NSW, 1442–1445.
  37. Subhajit Sahu. 2023. GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting. arXiv preprint arXiv:2312.13936 (2023).
  38. Subhajit Sahu. 2024. DF Louvain: Fast Incrementally Expanding Approach for Community Detection on Dynamic Graphs. arXiv preprint arXiv:2404.19634 (2024).
  39. A real-time detecting algorithm for tracking community structure of dynamic networks.
  40. Efficient closeness centrality computation for dynamic graphs. In Database Systems for Advanced Applications: 25th International Conference, DASFAA , Jeju, South Korea, September 24–27, , Proceedings, Part II. Springer, 534–550.
  41. Scalable community detection via parallel correlation clustering.
  42. Narrow scope for resolution-limit-free community detection. Physical Review E 84, 1 (2011), 016114.
  43. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9, 1 (Mar 2019), 5233.
  44. Node-grained incremental community detection for streaming networks. In IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 585–592.
  45. Effective and efficient dynamic graph coloring. Proceedings of the VLDB Endowment 11, 3 (2017), 338–351.
  46. N. Zarayeneh and A. Kalyanaraman. 2021. Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs. IEEE transactions on network science and engineering 8, 2 (Apr 2021), 1614–1629.
  47. An improved Louvain algorithm for community detection. Mathematical Problems in Engineering 2021 (2021), 1–14.
  48. An adaptive amoeba algorithm for shortest path tree computation in dynamic graphs. Information Sciences 405 (2017), 123–140.
  49. DynaMo: Dynamic community detection by incrementally maximizing modularity. IEEE Transactions on Knowledge and Data Engineering 33, 5 (2019), 1934–1945.
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