Community detection method based on vertex distance and clustering of density peaks

被引：0

作者：

Huang L. ^{[1
,2
]}

Li Y. ^{[1
,2
]}

Wang G.-S. ^{[1
,2
]}

Wang Y. ^{[1
,2
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun

[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2016年 / 46卷 / 06期

关键词：

Community detection; Complex network; Computer application; Density peaks clustering (DPC); Vertex distance;

D O I：

10.13229/j.cnki.jdxbgxb201606038

中图分类号：

学科分类号：

摘要：

Based on vertex similarity in complex network and density peaks clustering, a community detection method is proposed. First, a vertex distance calculation based on vertex similarity and the shortest distance between vertexes is proffered. Then, the density peaks clustering method is applied to detect the community structure in network. The density peaks clustering method not only allows the detection of the community centers to establish the epicenter for community expansion, but also avoids the process of selecting parameters. The proposed method is compared with the classic algorithms on both real-world networks and synthetic networks. Experimental results demonstrate that the proposed community detection method is practicable and effective. © 2016, Editorial Board of Jilin University. All right reserved.

引用

页码：2042 / 2051

页数：9

共 27 条

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