Community detection in complex networks via dissimilarity index and adaptive affinity propagation algorithm

摘要

Community detection in complex networks has received great attention due to its great potential practical applications in recent years. Many partition methods have been presented to find the community structure of a complex network. In this paper, we use the adaptive affinity propagation clustering method, associating with the modified dissimilarity index method which is employed to measure the dissimilarities between different vertices, to detect communities in networks. This method can determine the optimal number of communities and the corresponding membership assignment automatically. Simulation experiments on both computer-generated and real-world networks have shown the feasibility and efficiency of the proposed scheme.