Discovering network community based on multi-objective optimization

Article


Huang, Faliang, Zhang, Shichao and Zhu, Xiaofeng. 2013. "Discovering network community based on multi-objective optimization." Journal of Software. 24 (9), pp. 2062-2077. https://doi.org/10.3724/SP.J.1001.2013.04400
Article Title

Discovering network community based on multi-objective optimization

ERA Journal ID32139
Article CategoryArticle
AuthorsHuang, Faliang (Author), Zhang, Shichao (Author) and Zhu, Xiaofeng (Author)
Journal TitleJournal of Software
Journal Citation24 (9), pp. 2062-2077
Number of Pages16
Year2013
Place of PublicationBeijing, China
ISSN1796-217X
Digital Object Identifier (DOI)https://doi.org/10.3724/SP.J.1001.2013.04400
Web Address (URL)http://www.jos.org.cn/1000-9825/4400.htm
Abstract

Community discovery is an important task in mining complex networks, and has important theoretical and application value in the terrorist organization identification, protein function prediction, public opinion analysis, etc. However, existing metrics used to measure quality of network communities are data dependent and have coupling relations, and the community discovery algorithms based on optimizing just one metric have a lot of limitations. To address the issues, the task to discover network communities is formalized as a multi-objective optimization problem. An algorithm, MOCD-PSO, is used to discover network communities based on multi-objective particle swarm optimization, which constructs objective function with modularity Q, MinMaxCut and silhouette. The experimental results show that the proposed algorithm has good convergence and can find Pareto optimal network communities with relatively well uniform and dispersive distribution. In addition, compared with the classical algorithms based on single objective optimization (GN, GA-Net) and multi-objective optimization (MOGA-Net, SCAH-MOHSA), the proposed algorithm requires no input parameters and can discover the higher-quality community structure in networks.

Keywordscommunities mining; complex network; multi-objective particle swarm optimization
ANZSRC Field of Research 2020400604. Network engineering
400904. Electronic device and system performance evaluation, testing and simulation
350715. Quality management
Public Notes

© 2013 ISCAS.

Byline AffiliationsFujian Normal University, China
Guangxi Normal University, China
University of Queensland
Institution of OriginUniversity of Southern Queensland
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