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

-- Finding meaningful communities in social network has attracted the attentions of many researchers. The community structure of complex networks reveals both their organization and hidd en 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 r ange 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 th e 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 object s (like movies) by the means of rating is demonstrated by performing extensive experiments. With the emergence of social networks, people have been attracted to them, and have been sharing valuable information by means of communicating with each other. For example, folksonomies are social tagging sites which their users collaboratively express th eir feelings and sentiments toward a special resource like a movie or music by means of descriptive keywords (tags) [1] or ratings. One of the most important issues considered when analyzing these kinds of network s is community detection.