A general method of community detection by identifying community centers with affinity propagation

Detection of community structures is beneficial to analyzing the structures and properties of networks. It is of theoretical interest and practical significance in modern science. So far, a large number of algorithms have been proposed to detect community structures in complex networks, but most of them are suitable for a specific network structure. In this paper, a novel method (called CDMIC) is proposed to detect the communities in un-weighted, weighted, un-directed, directed and signed networks by constructing a dissimilarity distance matrix of network and identifying community centers with maximizing modularity. For a given network, we first estimate the distance between all pairs of nodes for constructing the dissimilarity distance matrix of the network. Then, this distance matrix is input to the affinity propagation (AP) algorithm to extract a candidate center set of community. Thirdly, we rank these centers in descending order according to the sum of their availability and responsibility. Finally, we determine the community structure by selecting the center subset from the candidate center set in an incremental manner to make the modularity maximization. On three real-world networks and some synthetic networks, experimental results show that our CDMIC method has higher performance in terms of classification accuracy and normalized mutual information (NMI), and ability to tolerate the resolution limitation.