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Enhanced density peak-based community detection algorithm

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Abstract

Density peak algorithm is a widely accepted density-based clustering algorithm, which shows excellent performance for the discrete data with any shape, any distribution and any density. However, the traditional density peak model is suitable for the complex network. To solve this problem, an enhanced density peak-based community detection algorithm is proposed in this paper, simply called DPCD. Firstly, a novel local density suitable for complex networks is defined to jointly consider the node distribution and network structure. Secondly, based on the node density and network structure, a density connected tree is constructed to measure a density following distance of each node. Finally, an improved density peak model is constructed to quickly and accurately cluster complex networks. Experiments on multiple synthetic networks and real networks show that our DPCD algorithm offers better community detection results.

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Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files]. Code availability: Code will be made available once the paper is accepted.

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Funding

This work is supported by the National Natural Science Foundation of China (No.62103143); Hunan Provincial Natural Science Foundation of China (No.2020JJ5199); the National Defense Basic Research Program of China (No.JCKY2019403D006); the National Key Research and Development Program (No.2019YFE0105300/2019YFE0118700); the Scientific Research Fund of Hunan Provincial Education Department (No.20C0786); and the Open Project of Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University (2020ICIP06).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Hunan University of Science and Technology, Taoyuan Street, Xiangtan, 411201, Hunan, China

    Lei Chen, Heding Zheng, Yuan Li, Zhaohua Liu & Lv Zhao

  2. School of Automation and Electronic Information, Xiangtan University, N.2nd Ring Road, Xiangtan, 411105, Hunan, China

    Hongzhong Tang

Authors
  1. Lei Chen
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  2. Heding Zheng
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  3. Yuan Li
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  4. Zhaohua Liu
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  5. Lv Zhao
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  6. Hongzhong Tang
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Correspondence to Lei Chen.

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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and is not under consideration for publication elsewhere, in whole or in part.

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Cite this article

Chen, L., Zheng, H., Li, Y. et al. Enhanced density peak-based community detection algorithm. J Intell Inf Syst 59, 263–284 (2022). https://doi.org/10.1007/s10844-022-00702-y

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  • DOI: https://doi.org/10.1007/s10844-022-00702-y

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