Abstract
Both Social Network Analysis (SNA) and Association Rules Learning (ARL) enriched our daily-lives through various applications, by improving axial roles in several domains. In particular, the community detection in online social networks (OSN) has interested researchers, for its valuable contribution in understanding systems complexity, as either for academic, commercial or further purposes. The aim of this paper is the identification of communities in OSN using knowledge extraction based on association rules methods. Furthermore, we propose a new approach, namely ARL Clustering, using association rules learning for SNA. Particularly, we base our detection on user’s friendships of OSN by processing a four level technique to extract meaningful rules, converted later to communities. The conducted experimentation was applied on two synthetic real-world networks, and improved important results in identifying potential communities in comparison with existing approaches.
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El-Moussaoui, M., Hanine, M., Kartit, A., Agouti, T. (2021). A Novel Approach of Community Detection Using Association Rules Learning: Application to User’s Friendships of Online Social Networks. In: Ben Ahmed, M., Rakıp Karaș, İ., Santos, D., Sergeyeva, O., Boudhir, A.A. (eds) Innovations in Smart Cities Applications Volume 4. SCA 2020. Lecture Notes in Networks and Systems, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-66840-2_3
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