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A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Author

Listed:
  • Lin Yu

    (School of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, China
    These authors contributed equally to this work.)

  • Xiaodan Guo

    (Northern Information Control Research Institute Group Co., Jiangjun Street, Nanjing 211153, China)

  • Dongdong Zhou

    (Northern Information Control Research Institute Group Co., Jiangjun Street, Nanjing 211153, China)

  • Jie Zhang

    (School of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, China
    These authors contributed equally to this work.)

Abstract

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

Suggested Citation

  • Lin Yu & Xiaodan Guo & Dongdong Zhou & Jie Zhang, 2024. "A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks," Mathematics, MDPI, vol. 12(10), pages 1-20, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1486-:d:1391987
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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.
    2. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    3. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.
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    Cited by:

    1. Lin Yu & Xin Zhao & Ming Lv & Jie Zhang, 2025. "A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection," Mathematics, MDPI, vol. 13(2), pages 1-22, January.

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