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Discovering global network communities based on local centralities

Published:03 March 2008Publication History
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Abstract

One of the central problems in studying and understanding complex networks, such as online social networks or World Wide Web, is to discover hidden, either physically (e.g., interactions or hyperlinks) or logically (e.g., profiles or semantics) well-defined topological structures. From a practical point of view, a good example of such structures would be so-called network communities. Earlier studies have introduced various formulations as well as methods for the problem of identifying or extracting communities. While each of them has pros and cons as far as the effectiveness and efficiency are concerned, almost none of them has explicitly dealt with the potential relationship between the global topological property of a network and the local property of individual nodes. In order to study this problem, this paper presents a new algorithm, called ICS, which aims to discover natural network communities by inferring from the local information of nodes inherently hidden in networks based on a new centrality, that is, clustering centrality, which is a generalization of eigenvector centrality. As compared with existing methods, our method runs efficiently with a good clustering performance. Additionally, it is insensitive to its built-in parameters and prior knowledge.

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      • Published in

        cover image ACM Transactions on the Web
        ACM Transactions on the Web  Volume 2, Issue 1
        February 2008
        280 pages
        ISSN:1559-1131
        EISSN:1559-114X
        DOI:10.1145/1326561
        Issue’s Table of Contents

        Copyright © 2008 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 3 March 2008
        • Accepted: 1 October 2007
        • Revised: 1 February 2007
        • Received: 1 January 2006
        Published in tweb Volume 2, Issue 1

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