Evaluating the effect of topic consideration in identifying communities of rating-based social networks

TL;DR

This paper investigates how incorporating topic analysis into community detection enhances the identification of meaningful communities in rating-based social networks, emphasizing the importance of content alongside network structure.

Contribution

It demonstrates the impact of topic consideration on community detection accuracy in social networks with rating data through extensive experiments.

Findings

Topic analysis improves community detection quality.

Content-aware methods outperform topology-only approaches.

Enhanced community relevance in social networks with shared ratings.

Abstract

Finding meaningful communities in social network has attracted the attentions of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structure of the network without performing any content analysis. Nowadays, real world social networks are containing a vast range of information including shared objects, comments, following information, etc. In recent years, a number of researches have proposed approaches which consider both the contents that are interchanged in the networks and the topological structures of the networks in order to find more meaningful communities. In this research, the effect of topic analysis in finding more meaningful communities in social networking sites in which the users express their feelings toward different objects (like movies) by the means of rating is demonstrated by performing extensive experiments.