Detecting Common Interest Kernels in Large Social Networks

In general, users may influence each other by their activities in social networks. It is interesting to explore hidden relationships of users based on their social activities. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a dataset from Epinions, which demonstrates that our method achieves 4%-11.8% accuracy improvement over the state of the art method.