An Evolutionary Approach for Detecting Communities in Social Networks

  • Koray OzturkEmail author
  • Faruk Polat
  • Tansel Özyer
Part of the Lecture Notes in Social Networks book series (LNSN)


Recent advancements and increasing use of social networking applications have made extensive amounts of data available. Because of this, exploring new and effective methods for mining and analyzing social network data is needed. In our work, a method inspired by evolutionary approach is proposed to find communities in social networks. A genetic algorithm, which is able to detect communities without needing the number of communities at the beginning of the algorithm, has been formulated and compared with other community detection methods to prove its accuracy, efficiency, and effectiveness. In addition, experiments using Newman’s spectral clustering method as a preprocessing step to our modified genetic algorithm have been done and seen producing better results for large datasets.


Social networks Community detection Genetic algorithms 


  1. 1.
    Scott, J.: Social Network Analysis: A Handbook. SAGE Publications, London (2000)Google Scholar
  2. 2.
    Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  3. 3.
    Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(47) (2002). MathSciNetCrossRefGoogle Scholar
  4. 4.
    Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge (2008)CrossRefGoogle Scholar
  5. 5.
    Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U.: Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford (2003). CrossRefGoogle Scholar
  7. 7.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Pastor-Satorras, R., Vespignani, A.: Evolution and Structure of the Internet: A Statistical Physics Approach, Cambridge University Press, New York (2004)CrossRefGoogle Scholar
  9. 9.
    Erdos, P., Renyi, A.: On random graphs I. Publ. Math. Debr. 6, 290–297 (1959)zbMATHGoogle Scholar
  10. 10.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Crane, D.: Invisible Colleges: Diffusion of Knowledge in Scientific Communities. University of Chicago Press, Chicago (1972)Google Scholar
  13. 13.
    Egghe, L., Rousseau, R.: Introduction to Informetrics. Elsevier, Amsterdam (1990)Google Scholar
  14. 14.
    Breiger, R.L., Boorman, S.A., Arabie, P.: An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. J. Math. Psychol. 12(3), 328–383 (1975). CrossRefGoogle Scholar
  15. 15.
    White, H.C., Boorman, S.A., Breiger, R.L.: Social structure from multiple networks. I. Blockmodels of roles and positions. Am. J. Sociol. 81(4), 730–780 (1977)Google Scholar
  16. 16.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. PNAS 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  17. 17.
    Girvan, M., Newman, M.E.J.: Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(2 Pt 2), 026113 (2004)Google Scholar
  18. 18.
    Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003). MathSciNetCrossRefGoogle Scholar
  19. 19.
    Holme, P., Huss, M., Jeong, H.: Subnetwork hierarchies of biochemical pathways. Bioinformatics 19, 532–538 (2003)CrossRefGoogle Scholar
  20. 20.
    Gleiser, P., Danon, L.: Community structure in Jazz. Adv. Complex Syst. 6(4), 565–573 (2003)CrossRefGoogle Scholar
  21. 21.
    Guimera, R., Danon, L., Diaz-Guilera, A., Giralt, F., Arenas, A.: Self-similar community structure in organisations. Phys. Rev. E 68, 065103 (2003)CrossRefGoogle Scholar
  22. 22.
    Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E Stat. Nonlin. Soft. Matter. Phys. 72(2), 027104 (2005)CrossRefGoogle Scholar
  23. 23.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)CrossRefGoogle Scholar
  24. 24.
    Fiedler, M.: Algebraic connectivity of graphs. Czechoslov. Math. J. 23(2), 298–305 (1973). Institute of Mathematics, Academy of Sciences of the Czech RepublicGoogle Scholar
  25. 25.
    Pothen, A., Simon, H.D., Liou, K.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Sys. Tech. J. 49(2), 291–308 (1970)CrossRefGoogle Scholar
  27. 27.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10) (2008)CrossRefGoogle Scholar
  28. 28.
    Richardson, T., Mucha, P.J., Porter, M.A.: Spectral tripartitioning of networks. Phys. Rev. Lett. E 80, 036111 (2009)CrossRefGoogle Scholar
  29. 29.
    Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  30. 30.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  31. 31.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
  32. 32.
    Darwin, C.: On the Origin of Species. John Murray, London (1859)Google Scholar
  33. 33.
    Tasgin, M., Bingol, H.: Community Detection in Complex Networks Using Genetic Algorithms. arXiv:cond-mat/0604419v1 (2006)Google Scholar
  34. 34.
    Tasgin, M., Herdagdelen, A., Bingol, H.: Community Detection in Complex Networks Using Genetic Algorithms. arXiv:0711.0491 (2007)Google Scholar
  35. 35.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)CrossRefGoogle Scholar
  36. 36.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E, 74(23), 8577–8582 (2006)MathSciNetGoogle Scholar
  37. 37.
    Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)CrossRefGoogle Scholar
  38. 38.
    Pissard, N., Assadi, H.: Detecting Overlapping Communities in Linear Time with PA Algorithm. arXiv:physics/0509254 (2005)Google Scholar
  39. 39.
    Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley, New York (1998)zbMATHGoogle Scholar
  40. 40.
    McAuley, J., Leskovec, J.: Learning to Discover Social Circles in Ego Networks, NIPS. Curran Associates Inc., Nevada (2012)Google Scholar
  41. 41.
    Boguna, M., Pastor-Satorras, R., Diaz-Guilera, A., Arenas A.: Models of social networks based on social distance attachment. Phys. Rev. E 70, 056122 (2004)CrossRefGoogle Scholar
  42. 42.
    Newman, M.E.J.: Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E, 64(1), 016131 (2001). American Physical SocietyGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Middle East Technical UniversityAnkaraTurkey
  2. 2.Department of Computer EngineeringTOBB University of Economics and TechnologyAnkaraTurkey

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