Abstract
We propose an efficient and novel approach for discovering communities in real-world random networks. Communities are formed by subsets of nodes in a graph, which are closely related. Extraction of these communities facilitates better understanding of such networks. Community related research has focused on two main problems, community discovery and community identification. Community discovery is the problem of extracting all the communities in a given network whereas community identification is the problem of identifying the community to which a given set of nodes from the network belong. In this paper we first perform a brief survey of the existing community-discovery algorithms and then propose a novel approach to discovering communities using bibliographic metrics. We also test the proposed algorithm on real-world networks and on computer-generated models with known community structures.
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