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

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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.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant no.61976216, and no.61672522.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Tianhao Shi, Shifei Ding & Xiao Xu

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

  3. Xuhai College, China University of Mining and Technology, Xuzhou, 221008, China

    Ling Ding

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  1. Tianhao Shi
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  4. Ling Ding
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Correspondence to Shifei Ding.

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Shi, T., Ding, S., Xu, X. et al. A community detection algorithm based on Quasi-Laplacian centrality peaks clustering. Appl Intell 51, 7917–7932 (2021). https://doi.org/10.1007/s10489-021-02278-6

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