A General Method for Detecting Community Structures in Complex Networks

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


We present a general method for detecting communities and their sub-structures in a complex network. The novelty of the method is to separate the network model and the community detection model. Network connectivity and influence spreading models are used as examples for network models. Depending on the network model, different communities and sub-structures can be found. We illustrate the results with two empirical network topologies. In these cases the strongest detected communities are very similar for the two network models. We use a community detection method that is based on searching local maxima of an influence measure describing interactions between nodes in a network.


Complex networks Community detection Influence spreading model Network connectivity Community influence measure 


  1. 1.
    Ball, M.O., Colbourn, C.J., Provan, J.S.: Network reliability. In: Handbooks in Operations Research and Management Science, vol. 7, pp. 673–762 (1995)Google Scholar
  2. 2.
    Barabási, A.-L.: Network Science. Cambridge University Press, Cambridge (2016)Google Scholar
  3. 3.
    Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min. 4(5), 512–546 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659(11), 1–44 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Karrer, B., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kuikka, V.: Influence spreading model used to community detection in social networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds.) Complex Networks & their applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol. 689, pp. 202–215. Springer, Cham (2018)Google Scholar
  8. 8.
    Kuikka, V.: Influence spreading model used to analyse social networks and detect Sub-communities. Comput. Soc. Netw. 5, 12 (2018). Scholar
  9. 9.
    Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009)CrossRefGoogle Scholar
  10. 10.
    Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11, 033015 (2009)CrossRefGoogle Scholar
  11. 11.
    Lusseau, D., Newman, M.E.J.: Identifying the role that animals play in their social networks. Proc. R. Soc. London Ser. B 271, S477 (2004)CrossRefGoogle Scholar
  12. 12.
    Newman, M.E.J.: Networks, An introduction. Oxford University Press, Oxford (2010)Google Scholar
  13. 13.
    Yang, Z., Algesheimer, R., Tessone, C.J.: A Comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, 30750 (2016).
  14. 14.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Finnish Defence Research AgencyRiihimäkiFinland

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