Community Detection in Real Large Directed Weighted Networks

Abstract

In this paper, the impact factors of edge weight and vertex weighted degree are introduced into community detection, and the directed weighted degree is used to measure the importance of the node. Based on the modularity optimization, a new community detecting algorithm with flexible and multitask architecture for directed and weighted networks is proposed. Then the community detection on the real large mail network is conducted, and the experimental results demonstrate that in large directed and weighted networks, the proposed algorithm is efficient with shorter execution time. By comparing the detecting results under different network types, we conclude that the use of weights can improve partition effectiveness and accuracy.