Handling topic dependencies alongside topology interactions using fuzzy inferences for discovering communities in social networks

Abstract

Social network analysis is one of the significant research areas. Discovering communities that satisfies simultaneously the topological and semantic requirements has become a challenging and useful topic in the analysis of social networks. Structural community detection algorithms do not consider node attributes. The semantic methods lead to the loss of valuable topological information. The combinatorial solutions favor one of the mentioned kinds. Moreover all and importantly, relationships between attributes and social topics have been neglected by existing methods. Content features in the real social networking space are not completely independent, but the strength of dependencies and relations is different. At first, this paper updates social network definitions to identify communities and adds attribute relationships. Then, we propose a two-phase method based on fuzzy inferences to basically combine structure and semantic and detect meaningful communities in social networks. Finally, two new metrics, Semantic Coherency and SSte are introduced to attributed relational and fairness measurements and are used along with Modularity as the three objective criteria of the proposed method to be maximized. Quantitative experiments reveal that the proposed method performs effectively regarding both network coherence and nodes attributes and achieves favorable outcomes to identify communities.