Discovering Effective Communities in Social Networks Using Fuzzy Semantic Relations Elicitation

Discovering Communities is a fundamental problem in understanding network performance for social network analysis. Traditional community detection methods merely consider the network topologies. Nonetheless, social media contains valuable data about people's interests, concerns, and sentiments, which is not reflected in the structure. The semantic solutions eventuate to the mislay of precious structural information. Most existing combinatorial methods favor one of the mentioned kinds and have limited performance. This paper introduces a 2-phase way based on fuzzy inferences to determine effective social network communities.  A series of real-life and synthetic networks have been used to evaluate the proposed method compared with several relevant algorithms. The experimental results proved that the proposed approach performs better in detecting meaningful communities and is more effective concerning network coherence and node attributes.