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Graph Mining and Communities Detection

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Business Intelligence (eBISS 2011)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 96))

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Summary

The incredible rising of on-line social networks gives a new and very strong interest to the set of techniques developed since several decades to mining graphs and social networks. In particular, community detection methods can bring very valuable informations about the structure of an existing social network in the Business Intelligence framework. In this chapter we give a large view, firstly of what could be a community in a social network, and then we list the most popular techniques to detect such communities. Some of these techniques were particularly developed in the SNA context, while other are adaptations of classical clustering techniques. We have sorted them in following an increasing complexity order, because with very big graphs the complexity can be decisive for the choice of an algorithm.

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Author information

Authors and Affiliations

  1. MAS Laboratory, Business Intelligence Team, Ecole Centrale Paris, Châtenay-Malabry, France

    Etienne Cuvelier & Marie-Aude Aufaure

Authors
  1. Etienne Cuvelier
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  2. Marie-Aude Aufaure
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Editor information

Editors and Affiliations

  1. MAS Laboratory, Ecole Centrale Paris, 92 295, Châtenay-Malabry Cedex, France

    Marie-Aude Aufaure

  2. Department of Computer & Decision Engineering (CoDE) CP 165/15, Universite Libre de Bruxelles, 1050, Brussels, Belgium

    Esteban Zimányi

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Cuvelier, E., Aufaure, MA. (2012). Graph Mining and Communities Detection. In: Aufaure, MA., Zimányi, E. (eds) Business Intelligence. eBISS 2011. Lecture Notes in Business Information Processing, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27358-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-27358-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27357-5

  • Online ISBN: 978-3-642-27358-2

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