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A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Author

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  • Wenxin Zhu

    (School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

  • Huan Li

    (School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

  • Wenhong Wei

    (School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China)

Abstract

Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a problem that is at least as hard as the hardest problems in nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance in community detection. Given that distinct crossover operators are suitable for various stages of algorithm evolution, we propose a two-stage algorithm that uses an individual similarity parameter to divide the algorithm into two stages. We employ appropriate crossover operators for each stage to achieve optimal performance. Additionally, a repair operation is applied to boundary-independent nodes during the second phase of the algorithm, resulting in improved community partitioning results. We assessed the effectiveness of the algorithm by measuring its performance on a synthetic network and four real-world network datasets. Compared to four existing competing methods, our algorithm achieves better accuracy and stability.

Suggested Citation

  • Wenxin Zhu & Huan Li & Wenhong Wei, 2023. "A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks," Mathematics, MDPI, vol. 11(12), pages 1-13, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2702-:d:1171156
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    References listed on IDEAS

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    1. Gong, Maoguo & Ma, Lijia & Zhang, Qingfu & Jiao, Licheng, 2012. "Community detection in networks by using multiobjective evolutionary algorithm with decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4050-4060.
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