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Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application

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Author Info

  • Vincent Labatut

    ()
    (Bit Lab - Galatasaray University)

  • Jean-Michel Balasque

    ()
    (Galatasaray University - Business & Marketing Department - Galatasaray University)

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    Abstract

    Community detection is an important part of network analysis and has become a very popular field of research. This activity resulted in a profusion of community detection algorithms, all different in some not always clearly defined sense. This makes it very difficult to select an appropriate tool when facing the concrete task of having to identify and interpret groups of nodes, relatively to a system of interest. In this article, we tackle this problem in a very practical way, from the user's point of view. We first review community detection algorithms and characterize them in terms of the nature of the communities they detect. We then focus on the methodological tools one can use to analyze the obtained community structure, both in terms of topological features and nodal attributes. To be as concrete as possible, we use a real-world social network to illustrate the application of the presented tools, and give examples of interpretation of their results from a Business Science perspective.

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    File URL: http://hal.archives-ouvertes.fr/docs/00/70/18/85/PDF/labatut2011b.pdf
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    Bibliographic Info

    Paper provided by HAL in its series Post-Print with number hal-00633653.

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    Date of creation: 2012
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    Publication status: Published, Computational Social Networks: Tools, Perspectives and Applications, Springer (Ed.), 2012, 81-113
    Handle: RePEc:hal:journl:hal-00633653

    Note: View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00633653
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    Related research

    Keywords: Complex Networks; Community detection; Business Science; Community interpretation;

    This paper has been announced in the following NEP Reports:

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    1. R. Luce, 1950. "Connectivity and generalized cliques in sociometric group structure," Psychometrika, Springer, vol. 15(2), pages 169-190, June.
    2. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model-based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354.
    3. Pras, Bernard & Evrard, Yves & Roux, Elyette, 2003. "Market : ├ętudes et recherches en marketing," Economics Papers from University Paris Dauphine 123456789/654, Paris Dauphine University.
    4. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, University of Chicago Press, vol. 34(4), pages 441-458, 05.
    5. G. Agarwal & D. Kempe, 2008. "Modularity-maximizing graph communities via mathematical programming," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 66(3), pages 409-418, December.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
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