Title

Discovering Communities In Complex Networks

Keywords

Community discovery/identification; Graph clustering

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. Copyright 2006 ACM.

Publication Date

12-1-2006

Publication Title

Proceedings of the Annual Southeast Conference

Volume

2006

Number of Pages

280-285

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1185448.1185512

Socpus ID

34248393021 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/34248393021

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