Community Detection in Social Networks Considering Topic Correlations

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To achieve better community detection results and to better understand the fundamental community semantics, we investigate the correlations of different topics in community detection model. This work models the formation of each edge assuming that users are more likely to communicate with each other when they are in the same community and their topics are closely correlated. A Topic Correlations based Community Detection (TCCD) model is proposed, which can learn community structure and semantic interpretation of each community. Our model is evaluated on two real datasets and is compared with four state-of-the-art methods. Experimental results show that TCCD significantly improves the accuracy of community detection. Finally, a case study shows that TCCD can detect the topic correlations inside a community. And we can infer better semantic interpretation of each community.

Authors

• Yingkui Wang
• Di Jin Tianjin University
• Katarzyna Musial
• Jianwu Dang Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

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