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A Self-organized Approach for Detecting Communities in Networks

  • Ben Collingsworth
  • Ronaldo Menezes
Part of the Studies in Computational Intelligence book series (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.

Keywords

Spectral Cluster Community Detection Green Community Current Community Entropy Calculation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Computer Sciences, BioComplex LaboratoryFlorida Institute of TechnologyMelbourneUSA

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