Community detection in social networks using structural and content information

Community detection in social networks is an area, which has witnessed many studies in recent years, and therefore several algorithms have been proposed. The majority of these methods are based on the relationships among users (structural information) to identify communities in social networks. However, these methods take into account only the strength of connections among users, but they ignore the content information such as the topics shared by users. In this paper, we propose a method of community detection in social networks that combines the content and the structural information. To meet this end, first, we propose a new approach to detect the topics involved in social networks by exploit the statistical and semantic measures. Second, we divide users into different groups according to their topics of interest, and then we perform a static community detection algorithm to detect communities in each group of users. The experimental results on real life datasets have shown that our method finding extracts more meaningful communities in social networks, and improves the quality of communities from the perspective of topics and links.