Community Detection by Local Influence

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 206)


We present a new algorithm to discover overlapping communities in networks with a scale free structure. This algorithm is based on a node evaluation function that scores the local influence of a node based on its degree and neighbourhood, allowing for the identification of hubs within a network. Using this function we are able to identify communities, and also to attribute meaningful titles to the communities that are discovered. Our novel methodology is assessed using LFR benchmark for networks with overlapping community structure and the generalized normalized mutual information (NMI) measure. We show that the evaluation function described is able to detect influential nodes in a network, and also that it is possible to build a well performing community detection algorithm based on this function.


graph theory link analysis centrality community detection overlapping communities 


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  1. 1.
    Figueira, Á., et al.: Breadcrumbs: A social network based on the relations established by collections of fragments taken from online news, (retrieved January 19, 2012)
  2. 2.
    Barabási, A.-L.: Scale-free networks: a decade and beyond. Science 325(5939), 412–413 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cohen, R., Havlin, S., Ben-Avraham, D.: Structural properties of scale free networks (2002)Google Scholar
  4. 4.
    Cravino, N., Devezas, J., Figueira, Á.: Using the Overlapping Community Structure of a Network of Tags to Improve Text Clustering. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media HT 2012 (2012)Google Scholar
  5. 5.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Kleinberg, J.M.: Hubs, authorities, and communities. ACM Computing Surveys 31(4es), 5–es (1999)CrossRefGoogle Scholar
  7. 7.
    Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80(1), 9 (2009)CrossRefGoogle Scholar
  8. 8.
    Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11(3), 033015 (2009)CrossRefGoogle Scholar
  9. 9.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Physical Review E - Statistical, Nonlinear and Soft Matter Physics 78(4 Pt. 2), 6 (2008)Google Scholar
  10. 10.
    Palla, G., Derényi, 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
  11. 11.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping Community Detection in Networks: the State of the Art and Comparative Study. Arxiv preprint arXiv11105813, V, pp. 1–30 (November 2011)Google Scholar
  12. 12.
    Xie, J., Szymanski, B.K., Liu, X.: SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process (2011)Google Scholar
  13. 13.
    Yang, J., Leskovec, J.: Structure and Overlaps of Communities in Networks. Arxiv preprint arXiv12056228 (2012)Google Scholar
  14. 14.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33(4), 452–473 (1977)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.CRACS/INESC TEC, Faculdade de CiênciasUniversidade do PortoPortoPortugal

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