Identifying influence for community in complex networks

Abstract

Community property has been found in many real complex networks. Identifying influence of community is open issue in complex networks. In this paper, we develop method for identifying influence of community, in which community structure is divided by hierarchical agglomerative algorithm (HAA), communities is converted nodes using renormaliztion process, and then a renormalized network is obtained. Then, using state of critical functionality (SCF), influences of nodes of renormalized networks are identified. The proposed method is applied to analyze influence of community of 9/11 terrorist network. The results show that the method is efficient in identifying influential community of complex networks.