Community Detecting by Signal and Flooding Algorithm on Complex Networks

Abstract

Community detecting has been the focus of many recent efforts on complex networks. In this paper, we propose a new community detecting algorithm. By signal transmission process on complex networks with Flooding algorithm, influence vectors of each node are got, and topological structure of each node is translated into geometrical relationships of spatial vectors. Thus, according to the nature of the clustering, the network structure of module is detected. In order to get the feasible spatial vectors, this paper puts forward the optimum passes method, which allows the reduction of the searching space and speeds up the convergence of algorithm. Thus, the search capability of community detecting is effectively strengthened. The proposed algorithm is tested on both computer-generated and real-world networks, and is compared with current representative algorithms in community mining. The evidence indicates that the algorithm is feasible and high accuracy.