Overlapping community detection in rating-based social networks through analyzing topics, ratings and links

Abstract

Owing to advances in information technology, online communications between people living in different parts of the world have considerably increased. The subsequent emergence of social networks helped this kind of communications to be further organized. One of the most important issues considered when analyzing these kinds of networks is community detection, in which a majority of studies tend to detect disjoint communities through analyzing linkages of networks. What this paper aims to achieve is to obtain overlapping communities in which the members have the same topics of interest, and where the strengths of connections between them are the consequence of their communications’ content analysis. Consequently, we have hereby proposed a generic framework for overlapping community detection in social networks with special focus on rating-based social networks. This framework considers the information shared by the users (ratings), as well as their topics of interest, for the sake of finding meaningful communities. This will lead us to topical communities in which members are interested in the same topics, and the strengths of their relationships are directly based on the rate of their viewpoints’ unity. Quantitative evaluations also reveal that the framework presented in this study achieves favorable results which are quite superior to the results of 3 other relevant frameworks in the literature.