A community detection algorithm based on Quasi-Laplacian centrality peaks clustering

Abstract

Searching for key nodes in social networks and clustering communities are indispensable components in community detection methods. With the wide application demand of detecting community networks, more and more algorithms have been proposed. Laplacian centrality peaks clustering (LPC) is an efficient and simple algorithm which is proposed on the basis of density peaks clustering (DPC) to identify clusters without parameters and prior knowledge. Before LPC is widely applied in community detection algorithms, some shortcomings should be addressed. Firstly, LPC fails to search for key nodes in networks accurately because of the similarity calculation method. Secondly, it takes too much time for LPC to calculate the Laplacian centrality of each point. To address these issues, a community detection algorithm based on Quasi-Laplacian centrality peaks clustering (CD-QLPC) is proposed after studying the advantages of Quasi-Laplacian centrality which can replace density or Laplacian centrality to characterize the importance of nodes in networks. Quasi-Laplacian centrality is obtained by the degree of each node directly, which needs less time than Laplacian centrality. In addition, a trust-based function is utilized to obtain the similarity accurately. Moreover, a new modularity-based merging strategy is adopted to identify the optimal number of communities adaptively. Experimental results show that CD-QLPC outperforms many state-of-the-art methods on both real-world networks and synthetic networks.