基于边密度聚类的重叠社区发现算法

doi:10.16451/j.cnki.issn1003-6059.201808002

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Abstract

Community detection based on edge clustering is capable of detecting overlapping communities naturally. However, it engenders the problems of obscure belongingness of the nodes on community borders and the excessive overlap of communities. In this paper, an overlapping community detection based on edge density clustering(OCDEDC) algorithm is proposed. Firstly, density clustering based on edges is employed to extract core edge communities. Next, a partitioning strategy is designed to dispatch border edges to its closest core edge community. In addition, a strategy based on the degrees and community belongingness of edges is designed to handle the isolated edges, and thus the excessive overlap of communities is avoided. Finally, edge communities are transformed back into node communities as the output. Experiments on artificial and real datasets show that the proposed algorithm detects overlapping communities efficiently and effectively.

Key words： Overlapping Community Complex Network Edge Clustering Density Clustering

Received: 03 April 2018

ZTFLH:

TP 391

Fund:

Supported by National Natural Science Foundation of China(No.61300104,61300103,61672158), Natural Science Foundation of Fujian Province(No.2013J01230,2014J01232), Fujian Province High School Science Fund for Distinguished Young Scholars(No.JA12016), Program for New Century Excellent Talents in Fujian Province University(No.JA13021), Fujian Natural Science Funds for Distinguished Young Scholar(No.2014J06017,2015J06014), Technology Innovation Platform Project of Fujian Province(No.2009J1007,2014H2005), Industry-Academy Cooperation Project(No.2014H6014,2017H6008), Haixi Government Big Data Application Cooperative Innovation Center

Corresponding Authors: GUO Wenzhong(Corresponding author), Ph.D., professor. His research interests include computational intelligence and its application.

About author:: GUO Kun, Ph.D., associate professor. His research interests include complex network analysis and data mining. CHEN Erbao, master student. His research interests include community detection.

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Cite this article:

GUO Kun,CHEN Erbao,GUO Wenzhong. Overlapping Community Detection Based on Edge Density Clustering[J]. , 2018, 31(8): 693-703.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201808002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I8/693

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