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A Consensus Community-Based Spider Wasp Optimization for Dynamic Community Detection

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.)

  • Xin Zhao

    (National Key Laboratory of Information Systems Engineering, Nanjing Research Institute of Electronic Engineering, Huitong Street, Nanjing 210007, China)

  • Ming Lv

    (School of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, 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

There are many evolving dynamic networks in the real world, and community detection in dynamic networks is crucial in many complex network analysis applications. In this paper, a consensus community-based discrete spider wasp optimization (SWO) approach is proposed for the dynamic network community detection problem. First, the coding, initialization, and updating strategies of the spider wasp optimization algorithm are discretized to adapt to the community detection problem. Second, the concept of intra-population and inter-population consensus community is proposed. Consensus community is the knowledge formed by the swarm summarizing the current state as well as the past history. By maintaining certain inter-population consensus community during the evolutionary process, the population in the current time window can evolve in a similar direction to those in the previous time step. Experimental results on many artificial and real dynamic networks show that the proposed method produces more accurate and robust results than current methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:2:p:265-:d:1567562
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    References listed on IDEAS

    as
    1. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    2. 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.
    3. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    4. 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.
    5. 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.
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