A Self-Organized Approach For Detecting Communities in Networks
Collingsworth, Bennett C
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The community structure of networks reveals hidden information about the whole network structure that cannot be discerned using other topological properties. Yet, the importance of identifying community structure in networks to many fields such as medicine, social sciences and national security, calls for better approaches for performing this identification. The prevalent community detection algorithms, such as the one proposed by Girvan and Newman, utilize a centralized approach that is unlikely to scale to very large networks and does not handle dynamic networks. Further, existing algorithms provide limited information regarding community overlap, where an individual participates in multiple communities. 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 and independent decisions concerning the community to which they belong; we call our approach SOCIAL (Self-Organized Community Identification ALgorithm). Node entropy is a defined as an indicator 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. The proposed algorithm offers several advantages over existing algorithms including near-linear performance, identification of partial community overlaps, and handling of dynamic changes in the network in a localized manner.