A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Wen, X., Chen, W.-N., Lin, Y., Gu, T., Zhang, H., Li, Y. , Yin, Y. and Zhang, J. (2017) A maximal clique based multiobjective evolutionary algorithm for overlapping community detection. IEEE Transactions on Evolutionary Computation, 21(3), pp. 363-377. (doi: 10.1109/TEVC.2016.2605501)

[img]
Preview
Text
133426.pdf - Published Version

1MB

Abstract

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Wen, X., Chen, W.-N., Lin, Y., Gu, T., Zhang, H., Li, Y., Yin, Y., and Zhang, J.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Evolutionary Computation
Publisher:IEEE
ISSN:1089-778X
ISSN (Online):1941-0026
Published Online:02 September 2016
Copyright Holders:Copyright © 2016 IEEE
First Published:First published in IEEE Transactions on Evolutionary Computation 21(3):363-377
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record