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Journal of Intelligent Information Systems

Erschienen in:

12.04.2022

Enhanced density peak-based community detection algorithm

verfasst von: Lei Chen, Heding Zheng, Yuan Li, Zhaohua Liu, Lv Zhao, Hongzhong Tang

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2022

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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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Titel: Enhanced density peak-based community detection algorithm
verfasst von: Lei Chen
Heding Zheng
Yuan Li
Zhaohua Liu
Lv Zhao
Hongzhong Tang
Publikationsdatum: 12.04.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2022
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-022-00702-y

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