Detecting Communities in Social Networks Using Local Information

Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure. There has been much recent research on identifying communities in networks. However, most existing approaches require complete network information, which is impractical for some networks, e.g. the World Wide Web or the cell phone telecommunication network. Local community detection algorithms have been proposed to solve the problem but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We also propose a community discovery process for large networks that iteratively finds communities based on our measure. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach.