Community Detection in Social Networks Considering Social Behaviors

The study of community detection in networks has drawn great attention in recent years. To find communities and to understand community semantics, both network topology and network content are utilized. Unfortunately, none of them can explain the driving factors of generating community structure with semantics, which is significant for understanding the mechanisms of community generation. Our observations on a large number of networks show that specific user social behaviors are underlying factors for the generation of community structure. We exploit four types of social behaviors that widely exist in networks, i.e., reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics. We investigate their impacts on the formation process of links and content in networks, during which communities with topics form. Our analysis shows that they are highly related to community structure. Consequently, a generative community detection model SBCD (social behavior-based community detection) is proposed by combining network topology and content, in which the above social behaviors play a core role. The model is evaluated on two real datasets. The experimental results show that SBCD outperforms state-of-the-art baselines. Finally, a case study illustrates several significant observations with respect to the proposed social behaviors.

Automated summary

The study of community detection in networks has gained significant attention, particularly in understanding the formation of community structure based on specific user social behaviors. By exploring reciprocity of interactions, posting preference, multitopic preference, and temporal variation of topics, researchers have proposed a generative community detection model, SBCD, that combines network topology and content and outperforms existing baselines in detecting communities with topics.