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A multi-objective evolutionary algorithm based on mixed encoding for community detection

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Abstract

Community structure is one of the most significant features in complex networks and community detection is a crucial method to analyze community structure. Existing representations in community detection have the characteristics of inflexibility and easily generate invalid solutions. To address the drawbacks, this paper proposed a multi-objective evolutionary algorithm based on mixed encoding (MOGAME). The algorithm combines the locus-based representation and labels-based representation, which can avoid generating invalid solution and improve the performance. Extensive experiments on both synthetic and real-word networks show that the proposed algorithm performs better than the existing algorithms with respect to accuracy and stability.

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Data availability statement

The datasets generated during and/or analysed during the current study are not publicly available due [REASON(S) WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.

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Acknowledgments

This work was supported by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grant No. 2018AAA0101301), the Key Projects of Artificial Intelligence of High School in Guangdong Province (No. 2019KZDZX1011), Innovation Project of High School in Guangdong Province (No. 2018KTSCX314), Dongguan Social Development Science and Technology Project (No. 20211800904722) and Dongguan Science and Technology Special Commissioner Project (No. 20201800500442).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Simin Yang, Wenhong Wei & Yuhui Zhang

  2. School of Computer and Information, Dongguan City College, Dongguan, 523419, China

    Qingxia Li

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  1. Simin Yang
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Correspondence to Wenhong Wei.

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Yang, S., Li, Q., Wei, W. et al. A multi-objective evolutionary algorithm based on mixed encoding for community detection. Multimed Tools Appl 82, 14107–14122 (2023). https://doi.org/10.1007/s11042-022-13846-4

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