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

Lin Yu, Xin Zhao, Ming Lv and Jie Zhang ()
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Lin Yu: School of Automation, Nanjing University of Science and Technology, Xiaolingwei Street, Nanjing 210094, China
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

Mathematics, 2025, vol. 13, issue 2, 1-22

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.

Keywords: complex networks; community detection; heuristic algorithm; spider wasp optimization; consensus community; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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