Review on community detection algorithms in social networks

With the development of Internet and computer science, more and more people join social networks. People communicate with each other and express their opinions on the social media, which forms a complex network relationship. Individuals in the social networks form a “relation structure” through various connections which produces a large amount of information dissemination. This “relation structure” is the community that we are going to research. Community detection is very important to reveal the structure of social networks, dig to people's views, analyze the information dissemination and grasp as well as control the public sentiment. In recent years, with community detection becoming an important field of social networks analysis, a large number of academic literatures proposed numerous methods of community detection. In this paper, we first describe the concepts of social network, community, community detection and criterions of community quality. Then we classify the methods of community detection from three classes: i) traditional algorithms of community detection; ii) algorithms of overlapping community detection; iii) algorithms of local community detection. And at last, we summarize and discuss these methods as well as the potential future directions of community detection.