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A Self-organized Approach for Detecting Communities in Networks

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Intelligent Distributed Computing VI

Part of the book series: Studies in Computational Intelligence ((SCI,volume 446))

Abstract

Community structure reveals hidden information about networks which cannot be discerned using other topological properties. The prevalent community detection algorithms, such as the Girvan-Newman algorithm, utilize a centralized approach which offers poor performance. We propose a self-organized approach to community detection which utilizes the newly introduced concept of node entropy to allow individual nodes to make decentralized decisions concerning the community to which they belong. Node entropy is a mathematical expression of an individual node’s satisfaction with its current community. As nodes become more “satisfied”, i.e., entropy is low, the community structure of a network is emergent. Our algorithm offers several advantages over existing algorithms including near-linear performance, identification of community overlaps, and localized management of dynamic changes in the network.

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Authors and Affiliations

  1. Computer Sciences, BioComplex Laboratory, Florida Institute of Technology, Melbourne, Florida, USA

    Ben Collingsworth & Ronaldo Menezes

Authors
  1. Ben Collingsworth
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  2. Ronaldo Menezes
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Corresponding author

Correspondence to Ben Collingsworth .

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Editors and Affiliations

  1. , Department of Electronics, Informatics, University of Calabria, Via P. Bucci, cubo 41C, Rende (CS), 87036, Italy

    Giancarlo Fortino

  2. Faculty of Automatics, Computers and Ele, Software Engineering Department, University of Craiova, Bvd. Decebal 107, Craiova, 200440, Romania

    Costin Badica

  3. , Dipartimento di Ingegneria Elettrica,, Università di Catania, V.le Andrea Doria, 6, Catania, 95125, Italy

    Michele Malgeri

  4. , Institute for Computer Science and, University of Duisburg-Essen, Schuetzenbahn 70, Essen, 45117, Germany

    Rainer Unland

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Collingsworth, B., Menezes, R. (2013). A Self-organized Approach for Detecting Communities in Networks. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds) Intelligent Distributed Computing VI. Studies in Computational Intelligence, vol 446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32524-3_6

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  • DOI: https://doi.org/10.1007/978-3-642-32524-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32523-6

  • Online ISBN: 978-3-642-32524-3

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