Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning
Abstract 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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-025-00715-w |
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| author | Mohamed El-Moussaoui Mohamed Hanine Ali Kartit Tarik Agouti |
| author_facet | Mohamed El-Moussaoui Mohamed Hanine Ali Kartit Tarik Agouti |
| author_sort | Mohamed El-Moussaoui |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-816be785be9b4a08bbbda09795827f3e |
| institution | DOAJ |
| issn | 2364-8228 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-816be785be9b4a08bbbda09795827f3e2025-08-20T03:03:24ZengSpringerOpenApplied Network Science2364-82282025-07-0110111810.1007/s41109-025-00715-wArlclustering: an R package for community detection in social networks based on user interaction and association rule learningMohamed El-Moussaoui0Mohamed Hanine1Ali Kartit2Tarik Agouti3ENSAJ - L.T.I. Laboratory, Chouaib Doukkali UniversityENSAJ - L.T.I. Laboratory, Chouaib Doukkali UniversityENSAJ - L.T.I. Laboratory, Chouaib Doukkali UniversityFSSM - I.S.I. Laboratory, Cadi Ayyad UniversityAbstract 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.https://doi.org/10.1007/s41109-025-00715-wSocial networks analysisCommunity detectionAssociation rulesClusteringARLClustering |
| spellingShingle | Mohamed El-Moussaoui Mohamed Hanine Ali Kartit Tarik Agouti Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning Applied Network Science Social networks analysis Community detection Association rules Clustering ARLClustering |
| title | Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning |
| title_full | Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning |
| title_fullStr | Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning |
| title_full_unstemmed | Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning |
| title_short | Arlclustering: an R package for community detection in social networks based on user interaction and association rule learning |
| title_sort | arlclustering an r package for community detection in social networks based on user interaction and association rule learning |
| topic | Social networks analysis Community detection Association rules Clustering ARLClustering |
| url | https://doi.org/10.1007/s41109-025-00715-w |
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