Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks.


Community detection Dynamic networks Community leaders Centrality measures 


  1. 1.
    Aggarwal, C., Subbian, K.: Evolutionary network analysis: a survey. ACM Comput. Surv. (CSUR) 47(1), 1–36 (2014)CrossRefGoogle Scholar
  2. 2.
    Bahmani, B., Chowdhury, A., Goel, A.: Fast incremental and personalized PageRank. Proc. VLDB Endow. 4(3), 173–184 (2010)CrossRefGoogle Scholar
  3. 3.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theor. Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  4. 4.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)CrossRefGoogle Scholar
  5. 5.
    Chen, P.Y., Hero, A.O.: Multilayer spectral graph clustering via convex layer aggregation: theory and algorithms. IEEE Trans. Sig. Inf. Process. Netw. 3, 553–567 (2017)Google Scholar
  6. 6.
    Cordeiro, M., Sarmento, R., Gama, J.: Dynamic community detection in evolving networks using locality modularity optimization. Soc. Netw. Anal. Min. 6(1), 15 (2016)CrossRefGoogle Scholar
  7. 7.
    Cordeiro, M., Sarmento, R.P., Brazdil, P., Gama, J.: Dynamic laplace: efficient centrality measure for weighted or unweighted evolving networks. CoRR abs/1808.02960 (2018)Google Scholar
  8. 8.
    Cordeiro, M., Sarmento, R.P., Brazdil, P., Gama, J.: Evolving networks and social network analysis methods and techniques. In: Višňovský, J., Radošinská, J. (eds.) Social Media and Journalism, chap. 7. IntechOpen, Rijeka (2018)Google Scholar
  9. 9.
    Desikan, P., Pathak, N., Srivastava, J., Kumar, V.: Incremental page rank computation on evolving graphs. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, WWW 2005, pp. 1094–1095. ACM, New York (2005)Google Scholar
  10. 10.
    Fortunato, S.: Community detection in graphs, June 2009Google Scholar
  11. 11.
    Hollocou, A., Maudet, J., Bonald, T., Lelarge, M.: A linear streaming algorithm for community detection in very large networks. CoRR abs/1703.02955 (2017)Google Scholar
  12. 12.
    Kas, M., Wachs, M., Carley, K.M., Carley, L.R.: Incremental algorithm for updating betweenness centrality in dynamically growing networks. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 33–40, August 2013Google Scholar
  13. 13.
    Kas, M., Carley, K.M., Carley, L.R.: Incremental closeness centrality for dynamically changing social networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013. pp. 1250–1258. ACM, New York (2013)Google Scholar
  14. 14.
    Khorasgani, R.R., Chen, J., Zaiane, O.R.: Top leaders community detection approach in information networks. In: Proceedings of the 4th Workshop on Social Network Mining and Analysis (2010)Google Scholar
  15. 15.
    Kim, K.S., Choi, Y.S.: Incremental iteration method for fast PageRank computation. In: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, IMCOM 2015, pp. 80:1–80:5. ACM, New York (2015)Google Scholar
  16. 16.
    Lancichinetti, A., Fortunato, S.: Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012)CrossRefGoogle Scholar
  17. 17.
    Leung, I.X., Hui, P., Liò, P., Crowcroft, J.: Towards real-time community detection in large networks. Nonlinear Soft Matter Phys. Phys. Rev. E - Stat. 79, 066107 (2009)Google Scholar
  18. 18.
    Li, J., Wang, X., Deng, K., Yang, X., Sellis, T., Yu, J.X.: Most influential community search over large social networks. In: Proceedings - International Conference on Data Engineering (2017)Google Scholar
  19. 19.
    Li, R.H., Qin, L., Ye, F., Yu, J.X., Xiaokui, X., Xiao, N., Zheng, Z.: Skyline community search in multi-valued networks. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2018)Google Scholar
  20. 20.
    Li, R.H., Qin, L., Yu, J.X., Mao, R.: Influential community search in large networks. Proc. VLDB Endowment 8, 509–520 (2015)CrossRefGoogle Scholar
  21. 21.
    Li, R.H., Qin, L., Yu, J.X., Mao, R.: Finding influential communities in massive networks. VLDB J. 26, 751–776 (2017)CrossRefGoogle Scholar
  22. 22.
    Nasre, M., Pontecorvi, M., Ramachandran, V.: Betweenness centrality - incremental and faster. CoRR abs/1311.2147 (2013)Google Scholar
  23. 23.
    Nguyen, N.P., Dinh, T.N., Tokala, S., Thai, M.T.: Overlapping communities in dynamic networks: their detection and mobile applications. In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM (2011)Google Scholar
  24. 24.
    Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM, pp. 2282–2290. IEEE (2011)Google Scholar
  25. 25.
    Oliveira, M.D.B., Gama, J.: An overview of social network analysis. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 2(2), 99–115 (2012)Google Scholar
  26. 26.
    Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)CrossRefGoogle Scholar
  27. 27.
    Qi, X., Duval, R.D., Christensen, K., Fuller, E., Spahiu, A., Wu, Q., Wu, Y., Tang, W., Zhang, C.: Terrorist networks, network energy and node removal: a new measure of centrality based on laplacian energy. Soc. Netw. 02(01), 19–31 (2013)CrossRefGoogle Scholar
  28. 28.
    Qi, X., Fuller, E., Wu, Q., Wu, Y., Zhang, C.Q.: Laplacian centrality: a new centrality measure for weighted networks. Inf. Sci. 194, 240–253 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Nonlinear Soft Matter Phys. Phys. Rev. E - Stat. 76, 036106 (2007)Google Scholar
  30. 30.
    Sariyuce, A.E., Kaya, K., Saule, E., Catalyiirek, U.V.: Incremental algorithms for closeness centrality. In: Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013, pp. 487–492 (2013)Google Scholar
  31. 31.
    Sarıyüce, A.E., Gedik, B., Jacques-Silva, G., Wu, K.L., Çatalyürek, Ü.V.: SONIC: streaming overlapping community detection. Data Min. Knowl. Discov. 30, 819–847 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Shah, D., Zaman, T.: Community detection in networks: the leader-follower algorithm. Sort 1050, 2 (2010)Google Scholar
  33. 33.
    Shang, J., Liu, L., Xie, F., Chen, Z., Miao, J., Fang, X., Wu, C.: A real-time detecting algorithm for tracking community structure of dynamic networks. In: 2012 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshops, SNAKDD, vol. 12 (2012)Google Scholar
  34. 34.
    Sun, H., Du, H., Huang, J., Li, Y., Sun, Z., He, L., Jia, X., Zhao, Z.: Leader-aware community detection in complex networks. Knowl. Inf. Syst. 1–30 (2019)Google Scholar
  35. 35.
    Wang, C.D., Lai, J.H., Yu, P.S.: Dynamic community detection in weighted graph streams. In: Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (2013)Google Scholar
  36. 36.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45, 43 (2013) CrossRefGoogle Scholar
  37. 37.
    Yakoubi, Z., Kanawati, R.: LICOD: a leader-driven algorithm for community detection in complex networks. Vietnam J. Comput. Sci. 1, 241–256 (2014)CrossRefGoogle Scholar
  38. 38.
    Yun, S.Y., Lelarge, M., Proutiere, A.: Streaming, memory limited algorithms for community detection. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  39. 39.
    Zhang, X., Zhu, J., Wang, Q., Zhao, H.: Identifying influential nodes in complex networks with community structure. Knowl.-Based Syst. 42, 74–84 (2013)CrossRefGoogle Scholar
  40. 40.
    Zhao, Z., Wang, X., Zhang, W., Zhu, Z.: A community-based approach to identifying influential spreaders. Entropy 17, 2228–2252 (2015)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Laboratory of Artificial Intelligence and Decision SupportPortoPortugal
  3. 3.Faculty of Science and TechnologyRyukoku UniversityKyotoJapan

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