A community detection method based on local similarity and degree clustering information

Community detection is of great importance to understand the structures and functions of networks. In this paper, a novel algorithm is proposed based on local similarity and degree clustering information. Local similarity is employed to measure the similarity between nodes and their neighbors in order to form communities within which nodes are closely connected. Degree clustering information, a hybrid criterion combining local neighborhood ratio with degree ratio, make a large number of nodes with low degree to embrace a small amount of nodes with high degree. Furthermore, each node in small scale communities has the duty to try to connect the nodes with high degree to expand communities, and finally the optimal community structure can be obtained. Simulation results on real and artificial networks show that the proposed algorithm has the excellent performance in terms of accuracy.

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