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A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Wen, Xuyun and Chen, Wei Neng and Lin, Ying and Gu, Tianlong and Zhang, Huaxiang and Li, Yun and Yin, Yilong and Zhang, Jun (2017) A maximal clique based multiobjective evolutionary algorithm for overlapping community detection. IEEE Transactions on Evolutionary Computation, 21 (3). pp. 363-377. ISSN 1089-778X

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Abstract

Detecting community structure has become one important technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can belong to only one community. However, in many realworld networks, communities are often overlapped with each other. Developing overlapping community detection algorithms thus becomes necessary. Along this avenue, this paper proposes a maximal clique based multiobjective evolutionary algorithm (MOEA) for overlapping community detection. In this algorithm, a new representation scheme based on the introduced maximal-clique graph is presented. Since the maximalclique 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 allows MOEAs to handle the overlapping 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: Article
ID code: 65262
Keywords: clique-based representation, maximal-clique graph, multiobjective evolutionary algorithm (MOEA), overlapping community detection, Electrical engineering. Electronics Nuclear engineering, Software, Theoretical Computer Science, Computational Theory and Mathematics
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering
Depositing user: Pure Administrator
Date deposited: 27 Aug 2018 12:01
Last modified: 22 Feb 2019 04:17
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URI: https://strathprints.strath.ac.uk/id/eprint/65262
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