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<p>ARLClustering is an open-source R package for community detection in social networks. Unlike traditional methods that rely on structural properties such as modularity, degree centrality, and clustering coefficient, ARLClustering leverages association rule mining (ARM) to identify meaningful interaction patterns based on users’ friendship activity. By analyzing frequent interaction rules, it uncovers communities that may be overlooked by purely structural approaches. The package offers a comprehensive set of functions tailored for social network analysis. It has been tested on real-world datasets, including the Karate Club, Dolphins, LesMiserables, NetScience, and Facebook networks. The results demonstrate its effectiveness in detecting communities and provide a comparative analysis against existing methods. ARLClustering is now available on the CRAN repository, with its source code accessible on GitHub. It serves as a valuable tool for researchers and practitioners, not only enhancing community detection techniques in social network analysis but also introducing a novel approach to uncover hidden communities through experimental studies on real-world social network datasets.</p>

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Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Mohamed El-Moussaoui,
  • Mohamed Hanine,
  • Ali Kartit,
  • Tarik Agouti

摘要

ARLClustering is an open-source R package for community detection in social networks. Unlike traditional methods that rely on structural properties such as modularity, degree centrality, and clustering coefficient, ARLClustering leverages association rule mining (ARM) to identify meaningful interaction patterns based on users’ friendship activity. By analyzing frequent interaction rules, it uncovers communities that may be overlooked by purely structural approaches. The package offers a comprehensive set of functions tailored for social network analysis. It has been tested on real-world datasets, including the Karate Club, Dolphins, LesMiserables, NetScience, and Facebook networks. The results demonstrate its effectiveness in detecting communities and provide a comparative analysis against existing methods. ARLClustering is now available on the CRAN repository, with its source code accessible on GitHub. It serves as a valuable tool for researchers and practitioners, not only enhancing community detection techniques in social network analysis but also introducing a novel approach to uncover hidden communities through experimental studies on real-world social network datasets.