Fuzzy Logic-Driven Community Detection in Social Networks Using Semantic Node Analysis
Keywords:
Community detection, Fuzzy logic-driven, Natural Language Processing (NLP), Semantic node analysis, Social networksAbstract
Community detection in social networks is critical for understanding structural and functional dynamics among users. Traditional methods often rely on topology-based clustering, overlooking the rich semantic context embedded within nodes. This paper proposes a novel fuzzy logic-driven approach that incorporates semantic node properties to enhance community detection accuracy. By integrating linguistic and contextual data from user profiles and interactions, our method dynamically evaluates node similarity and membership within overlapping communities. The proposed model uses a fuzzy inference system to handle uncertainty and partial membership, which are inherent characteristics of social data. Experimental evaluations on benchmark datasets demonstrate that our method outperforms conventional techniques in terms of accuracy and modularity. The approach effectively reveals nuanced community structures, offering valuable insights for applications in targeted marketing, recommendation systems, and social behavior analysis. Results validate the potential of combining fuzzy logic with semantic analysis for robust and intelligent community detection in complex, real-world social networks.
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