A Novel Edge Weighting Method to Enhance Network Community Detection

Community detection is one of the most popular issues in analyzing and understanding the networks. Existing works show that community detection can be enhanced by proper assignments of weights onto the edges of a network. Large numbers of edge weighting schemes have been developed to cope with this problem. However, hardly has a satisfied balance between the local and global weightings been found. In this paper, the problem of the local and global weighting balance is first proposed and discussed. The SimRank is next introduced as a novel edge weighting method. Furthermore, the fast Newman algorithm is extended to be applicable for a weighted network. Combined with the edge weighting techniques, the extended algorithm enhances the performance of the original algorithm significantly through exhaustive experiments. And by comparing with several weighting methods, the experiments demonstrate that the proposed algorithm is superior and more robust for different kinds of networks.