An iterated local search algorithm for community detection in signed networks

Yiran Chen, Qinma Kang, Wenqiang Duan, Yunfan Shan, Ran Xiao and Yunfan Kang
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Yiran Chen: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong 264209, P. R. China
Qinma Kang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong 264209, P. R. China
Wenqiang Duan: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong 264209, P. R. China
Yunfan Shan: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong 264209, P. R. China
Ran Xiao: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, Shandong 264209, P. R. China
Yunfan Kang: Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 08, 1-21

Abstract: Community detection in signed networks has become a research hotspot in the area of network science recently. Since the classical problem has great significance for theoretical analysis and practical application, many heuristics or metaheuristics have been presented. Despite some progress and results that have been achieved, it remains an open challenge to identify community structure in large signed networks. In this paper, we propose a simple and effective iterated local search algorithm coupled with a powerful local search mechanism to solve the community detection problem. Due to the limitation of modularity in resolution, the modularity density criterion is adopted to find communities in signed networks. Extensive experiments have been conducted on synthetic and real-world networks. The statistical analyses demonstrate that the proposed algorithm can provide high-quality solutions compared to the state-of-the-art algorithms.

Keywords: Signed networks; modularity density; metaheuristic; iterated local search (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122501054

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