Evolution Algorithm for Community Detection in Social Networks Using Node Centrality

  • Krista Rizman Žalik
Part of the Studies in Big Data book series (SBD, volume 40)


Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks like the internet, social networks, food networks, e-mail networks and biochemical networks. Automatic network clustering can uncover natural groups of nodes called communities in real networks that reveals its underlying structure and functions. In this paper, we use a multiobjective evolution community detection algorithm, which forms center-based communities in a network exploiting node centrality. Node centrality is easy to use for better partitions and for increasing the convergence of evolution algorithm. The proposed algorithm reveals the center-based natural communities with high quality. Experiments on real-world networks demonstrate the efficiency of the proposed approach.


Social networks Complex networks Multiobjective community detection Centrality 



This work was supported by the Slovenian Research Agency (grant no.: J2-8176, P2-0041).


  1. 1.
    Schaefer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)Google Scholar
  2. 2.
    Gargi, U., Lu, W., Mirrokni, V.S., Yoon, S.: Large-scale community detection on youtube for topic discovery and exploration. ICWSM (2011)Google Scholar
  3. 3.
    Aggarwal, C.C., Xie, Y., Philip, S.Y.: Towards community detection in locally heterogeneous networks. In: SDM, pp. 391-402 (2011)CrossRefGoogle Scholar
  4. 4.
    Aggrawal, R.: Bi-objective community detection (bocd) in networks using genetic algorithm. Contemp. Comput. 168(1), 5–15 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Shang, R., Bai, J., Jiao, L., Jin, C.: Community detection based on modularity and improved genetic algorithm. Stat. Mech. Appl. Phys. A 392(5), 1215–1231 (2012)CrossRefGoogle Scholar
  6. 6.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying clusters in networks. Proc. Natl Acad. Sci. U.S.A 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  7. 7.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(026113) (2004)Google Scholar
  8. 8.
    Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. U.S.A 104(1), 36–41 (2007)CrossRefGoogle Scholar
  9. 9.
    Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., Wagner, D.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172–188 (2008)CrossRefGoogle Scholar
  10. 10.
    Nadakuditi, X.R., Newman, M.: Spectra of random graphs with community structure and arbitrary degrees. Phys. Rev. E 89(4), 042816 (2014)CrossRefGoogle Scholar
  11. 11.
    Pizzuti, C.: Ga-net: a genetic algorithm for community detection in social networks. PPSN, 1081–1090 (2008)CrossRefGoogle Scholar
  12. 12.
    Shi, C., Yan, Z., Cai, Y., Wu, B.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12, 850–859 (2012)CrossRefGoogle Scholar
  13. 13.
    Rizman, Ž.K.: Maximal neighbor similarity reveals real communities in networks. Sci. Rep. 5 18374, 1–10 (2015)Google Scholar
  14. 14.
    Rizman, Ž.K.: Community detection in networks using new update rules for label propagation. Computing. 7(99), 679–700 (2017)Google Scholar
  15. 15.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948, (1995)Google Scholar
  16. 16.
    Dorigo, M., Caro, G.D.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the Congress on Evolutionary Computation. IEEE Press. pp. 1470–1477 (1999)Google Scholar
  17. 17.
    Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization-NICSO 2010, pp. 65–74 (2010)CrossRefGoogle Scholar
  18. 18.
    Rizman, Ž.K., Žalik, B.: Multi-objective evolutionary algorithm using problem specific genetic operators for community detection in networks. Neural Comput. Appl. 1–14 (2017).
  19. 19.
    Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012)CrossRefGoogle Scholar
  20. 20.
    Deb, K., Pratap, A., Agarwal, S.A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  21. 21.
    Corne, D., Jerram, N., Knowles, J., Oates, M.: PESA-II: Region-based selection in evolutionary multiobjective optimization. GECCO, 283–290 (2001)Google Scholar
  22. 22.
    Soland, R.: Multicriteria optimization: a general characterization of efficient solutions. Decis. Sci. 10(1), 26–38 (1979)CrossRefGoogle Scholar
  23. 23.
    Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems, In: Proceedings 3rd Annual Conference on Genetic Programming (GP’98), Madison, USA, pp. 568–575 (1998)Google Scholar
  24. 24.
    Freeman, L.C.: Centrality in social networks: conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar
  26. 26.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: Behav. Ecol. Sociobiol. 54, 396–405 (2003)CrossRefGoogle Scholar
  27. 27.
    Knuth, D.E.: The Stanford GraphBase: A Platform for Combinatorial Computing. Addison-Wesley, Reading (1993)zbMATHGoogle Scholar
  28. 28.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)zbMATHGoogle Scholar
  30. 30.
    Gong, M.G, Fu, B., Jiao, L.C., Du, H.F.: Memetic algorithm for community detection in networks. Phys. Rev. E 006100 (2011)Google Scholar
  31. 31.
    Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16, 418–430 (2012)CrossRefGoogle Scholar
  32. 32.
    Krebs, V.: The network was compiled by V. Krebs and is unpublished, but can found on Krebs’ web site. (Accessed: 10 December 2016)
  33. 33.
    Adamic, L.A., Glance, N.: The political blogosphere and the 2004 US Election, In: Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem, 36–43 (2005)Google Scholar
  34. 34.
    Gleiser, P., Danon, L.: Adv. Complex Syst. 6(4), 565–573 (2003)CrossRefGoogle Scholar
  35. 35.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R. Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

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