A Novel Community Detection Method Based on Cluster Density Peaks

Abstract

Community structure is the basic structure of a social network. Nodes of a social network can naturally form communities. More specifically, nodes are densely connected with each other within the same community while sparsely between different communities. Community detection is an important task in understanding the features of networks and graph analysis. At present there exist many community detection methods which aim to reveal the latent community structure of a social network, such as graph-based methods and heuristic-information-based methods. However, the approaches based on graph theory are complex and with high computing expensive. In this paper, we extend the density concept and propose a density peaks based community detection method. This method firstly computes two metrics-the local density $\rho$ and minimum climb distance $\delta$ -for each node in a network, then identify the nodes with both higher $\rho$ and $\delta$ in local fields as each community center. Finally, rest nodes are assigned with corresponding community labels. The complete process of this method is simple but efficient. We test our approach on four classic baseline datasets. Experimental results demonstrate that the proposed method based on density peaks is more accurate and with low computational complexity.