Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application
AbstractCommunity 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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Bibliographic InfoPaper provided by HAL in its series Post-Print with number hal-00633653.
Date of creation: 2012
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Publication status: Published, Computational Social Networks: Tools, Perspectives and Applications, Springer (Ed.), 2012, 81-113
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Complex Networks; Community detection; Business Science; Community interpretation;
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- 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.
- Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
- 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.
- 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.
- R. Luce, 1950. "Connectivity and generalized cliques in sociometric group structure," Psychometrika, Springer, vol. 15(2), pages 169-190, June.
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