Advertisement

An Algorithm to Find Overlapping Community Structure in Networks

  • Steve Gregory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702)

Abstract

Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorithm based on the betweenness centrality measure. Like the original algorithm, ours performs hierarchical clustering — partitioning a network into any desired number of clusters — but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.

Keywords

Short Path Collaboration Network Vertex Pair Good Split Edge Betweenness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Adamcsek, B., Palla, G., Farkas, I., Derényi, I., Vicsek, T.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22, 1021–1023 (2006)CrossRefGoogle Scholar
  2. 2.
    Baumes, J., Goldberg, M., Krishnamoorty, M., Magdon-Ismail, M., Preston, N.: Finding communities by clustering a graph into overlapping subgraphs. In: Proc. IADIS Applied Computing 2005, pp. 97–104 (2005)Google Scholar
  3. 3.
    Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient identification of overlapping communities. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 27–36. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Brandes, U., Gaertler, M., Wagner, D.: Experiments on graph clustering algorithms. In: Di Battista, G., Zwick, U. (eds.) ESA 2003. LNCS, vol. 2832, pp. 568–579. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  6. 6.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Li, X., Liu, B., Yu, P.S.: Discovering overlapping communities of named entities. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 593–600. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology 54, 396–405 (2003)CrossRefGoogle Scholar
  10. 10.
    Nelson, D.L., McEvoy, C.L., Schreiber, T.A.: The University of South Florida word association, rhyme and word fragment norms (1998), http://w3.usf.edu/FreeAssociation/
  11. 11.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)CrossRefGoogle Scholar
  12. 12.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577–8582 (2006)CrossRefGoogle Scholar
  14. 14.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)CrossRefGoogle Scholar
  15. 15.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  16. 16.
    Pinney, J.W.: Personal communicationGoogle Scholar
  17. 17.
    Pinney, J.W., Westhead, D.R.: Betweenness-based decomposition methods for social and biological networks. In: Barber, S., Baxter, P.D., Mardia, K.V., Walls, R.E. (eds.) Interdisciplinary Statistics and Bioinformatics, pp. 87–90. Leeds University Press (2006)Google Scholar
  18. 18.
    Xie, N.: Social network analysis of blogs. MSc dissertation. University of Bristol (2006)Google Scholar
  19. 19.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Steve Gregory
    • 1
  1. 1.Department of Computer Science, University of Bristol, BS8 1UBEngland

Personalised recommendations