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<p>Dynamic community detection is a critical challenge in modern social network analysis and artificial intelligence research. The goal is to improve the quality of community structures as networks evolve while minimizing discrepancies between consecutive community structures. This study introduces Transferring Cliques and Quasi-cliques Knowledge with Multi-Objective Bee Swarm Optimization, referred to as <i>TCK-MBSO</i>, a novel approach aimed at enhancing dynamic community detection performance. The core idea of <i>TCK-MBSO</i> is to transfer valuable clique and quasi-clique information from previous community structures to the current detection task, leveraging this knowledge to improve both accuracy and consistency over time. By embedding this transferred knowledge into a multi-objective bee swarm optimization framework, <i>TCK-MBSO</i> significantly enhances the detection of dynamic communities. Experimental results consistently demonstrate that <i>TCK-MBSO</i> outperforms state-of-the-art algorithms across various test scenarios, delivering superior results in terms of both community structure quality and continuity between snapshots.</p>

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Transferring clique knowledge in multi-objective bee swarm optimization for dynamic community detection

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Narimene Dakiche,
  • Fatima Benbouzid-Si Tayeb,
  • Karima Benatchba

摘要

Dynamic community detection is a critical challenge in modern social network analysis and artificial intelligence research. The goal is to improve the quality of community structures as networks evolve while minimizing discrepancies between consecutive community structures. This study introduces Transferring Cliques and Quasi-cliques Knowledge with Multi-Objective Bee Swarm Optimization, referred to as TCK-MBSO, a novel approach aimed at enhancing dynamic community detection performance. The core idea of TCK-MBSO is to transfer valuable clique and quasi-clique information from previous community structures to the current detection task, leveraging this knowledge to improve both accuracy and consistency over time. By embedding this transferred knowledge into a multi-objective bee swarm optimization framework, TCK-MBSO significantly enhances the detection of dynamic communities. Experimental results consistently demonstrate that TCK-MBSO outperforms state-of-the-art algorithms across various test scenarios, delivering superior results in terms of both community structure quality and continuity between snapshots.