摘要：The hierarchy clustering methods based on vertex similarity have the advantage that global evaluation can be incorporated for community discovery. Vertex similarity metric is the most important part of these methods. However, the existing methods perform not well for community discovery compared with the state-of-the-art algorithms. In this paper, we propose a new vertex similarity metric based on local flow model, called Local Flow Metric (LFM), for community discovery. LFM considers both the number of connecting paths and local edge density which are essential measures in community structure. Compared with the existing metrics of vertex similarity, LFM outperforms substantially in community discovery quality and the computing time. Furthermore, our LFM algorithm is superior to the state-of-the-art algorithms in some aspects.
