Evaluating the effect of topic consideration in identifying communities of rating-based social networks
This work addresses the problem of improving community detection for users in social networks by integrating content analysis, but it is incremental as it builds on existing methods that combine topology and content.
The paper investigates how incorporating topic analysis affects community detection in rating-based social networks, finding that it leads to more meaningful communities as demonstrated through extensive experiments.
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.