Multi-Criteria Discovery of Communities in Social Networks Based on Services

Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation, thereby clarifying its novelty and contribution. It applies multiple filtering criteria related to user attributes, accessed services, and interaction patterns. Experimental comparisons against Louvain, Hierarchical Agglomerative Clustering, Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity, 0.13 conductance, 0.77 coverage, and significantly fast response time of 9.8 s, even with 10,000 users and 400 services. Moreover, community discovery-based service consistently detects a larger number of communities with distinct topics of interest, underscoring its capacity to generate detailed and efficient structures in complex networks. These results confirm both the efficiency and effectiveness of the proposed method. Beyond controlled evaluation, communities discovery based service is applicable to targeted recommendations, group-oriented marketing, access control, and service personalization, where communities are shaped not only by user links but also by service engagement.