User-Interest based Community Extraction in Social Networks

The rapid evolution of modern social networks motivates the design of networks based on users’ interests. Using popular social media such as Facebook and Twitter, we show that this new perspective can generate more meaningful information about the networks. In this paper, we model userinterest based networks by deducing intent from social media activities such as comments and tweets of millions of users in Facebook and Twitter, respectively. This interactive content derives networks that are dynamic in nature as the user interests can evolve due to temporal and spatial activities occurring around the user. To understand and analyze these networks, we develop a new approach for mining communities to overcome the limitations of the widely used Clauset, Newman, and Moore (CNM) community detection algorithm. The key feature of the proposed approach is that the communities are extracted incrementally by removing the inuence of the communities identied in the previous steps. Experimental results show that our approach can nd many focused communities of similar interests compared to the large communities found by the CNM algorithm. Our user-interest based model and community extraction methodology together can be used to identify target communities in the context of business requirements.