Overlapping community detection based on link similarity clustering

被引：0

作者：

Zhang, Gui-Jie ^{[1
,2
]}

Zhang, Jian-Pei ^{[1
]}

Yang, Jing ^{[1
]}

Xin, Yu ^{[1
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, Heilongjiang

[2] College of Computer Science and Technology, Jilin Normal University, Siping, 136000, Jilin

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2015年 / 43卷 / 07期

关键词：

Community detection; Hierarchical clustering; Link community; Local link similarity metric; Overlapping community;

D O I：

10.3969/j.issn.0372-2112.2015.07.012

中图分类号：

学科分类号：

摘要：

Community structure is one of the most common and important social network topological properties. This paper proposes a link community detection algorithm based on hierarchical clustering. Firstly, the algorithm sets up similarity measure according to the degree distribution of links nearby; then sets up local link similarity clustering algorithm which takes the similarity matrix as input with the purpose of detecting the best link community; further more realizes link community detection effectively. And then, optimize the link community to solve the problem of excessive overlapping and isolated community. Experiment results based on real world and computer generated networks show that the algorithm is highly efficient. ©, 2015, Chinese Institute of Electronics. All right reserved.

引用

页码：1329 / 1335

页数：6

共 12 条

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