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Detecting Communities in Social Networks

  • Tsuyoshi MurataEmail author
Chapter

Abstract

There are many practical examples of social networks such as friendship networks or co-authorship networks. Detecting dense subnetworks from such networks are important for finding similar people and understanding the structure of factions. This chapter explains the definitions of communities, criteria for evaluating detected communities, methods for community detection, and actual tools for community detection.

References

  1. 1.
    Barber, M. J., Modularity and community detection in bipartite networks, Physical Review E, 76(066102), 1–9, 2007MathSciNetGoogle Scholar
  2. 2.
    Chakrabarti, D., Kumar, R., Tomkins, A., Evolutionary Clustering, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD06), pp. 554–560, 2006Google Scholar
  3. 3.
    Clauset, A., Fast modularity community structure inference algorithm, http://www.cs.unm.edu/~aaron/research/fastmodularity.htm
  4. 4.
    Clauset, A., Newman, M. E. J., Moore, C., Finding community structure in very large networks, Physical Review E, 70(066111), 1–6, 2004Google Scholar
  5. 5.
    Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A., Comparing community structure identification, Journal of Statistical Mechanics, P09008, 1–10, 2005Google Scholar
  6. 6.
    Fortunato, S., Community detection in graphs, Physics Reports, 486, 75–174, 2010CrossRefMathSciNetGoogle Scholar
  7. 7.
    Fortunato, S., Barthelemy, M., Resolution limit in community detection, Proceedings of the National Academy of Sciences (PNAS), 104(1), 36–41, 2007Google Scholar
  8. 8.
    Girvan, M., Newman, M. E. J., Community structure in social and biological networks Proceedings of the National Academy of Sciences (PNAS), 99(12), 7821–7826, 2002Google Scholar
  9. 9.
    Guimera, R., Sales-Pardo, M., Amaral, L. A. N., Module identification in bipartite and directed networks, Physical Review E, 76(036102), 1–8, 2007Google Scholar
  10. 10.
    Leskovec, J., Lang, K. J., Dasgupta, A., Mahoney, M. W., community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters, arXiv:0810.1355, http://arxiv.org/abs/0810.1355, 2008
  11. 11.
    Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B. L., FacetNet: A Framework for Analyzing Communities and Their Evolutions in Dynamic Networks, Proceedings of the 17th International World Wide Web Conference (WWW2008), pp. 685–694, 2008Google Scholar
  12. 12.
    Newman, M. E. J., Modularity and community structure in networks, Proceedings of the National Academy of Sciences (PNAS), 103(23), 8577–8582, 2006Google Scholar
  13. 13.
    Newman, M. E. J., Network data, http://www-personal.umich.edu/~mejn/netdata/
  14. 14.
    Newman, M.E.J., Girvan, M., Finding and evaluating community structure in networks, Physical Review E, 69(026113), 1–16, 2004Google Scholar
  15. 15.
    Palla, G., DerE’nyi, I., Farkas, I., Vicsek, T., Uncovering the overlapping community structure of complex networks in nature and society, Nature 435, 814–818, 2005CrossRefGoogle Scholar
  16. 16.
    Vakali, A., Kompatsiaris, I., Detecting, understanding and exploiting web communities, http://www2009.org/tutorials.html, 2009
  17. 17.
    Wakita, K., Ken Wakita – Community analysis software, http://www.is.titech.ac.jp/~wakita/en/software/community-analysis-so-ftware/
  18. 18.
    Xu, J., Chen, H., The topology of dark networks, Communications of the ACM, 51(10), 58–65, 2008CrossRefGoogle Scholar
  19. 19.
    Zhou, H., Network landscape from a Brownian particlefs perspective, Physical Review E 67(041908), 1–5, 2003Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyTokyoJapan

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