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CLC number: TP391

On-line Access: 2012-11-02

Received: 2012-03-05

Revision Accepted: 2012-07-09

Crosschecked: 2012-10-12

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.11 P.828-839

http://doi.org/10.1631/jzus.C1200049


Overlapping community detection combining content and link


Author(s):  Zhou-zhou He, Zhong-fei (Mark) Zhang, Philip S. Yu

Affiliation(s):  Zhejiang Provincial Key Laboratory of Information Network Technology, Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zju_hzz@zju.edu.cn, zhongfei@zju.edu.cn, psyu@uic.edu

Key Words:  Overlapping, Content, Link, Community detection


Zhou-zhou He, Zhong-fei (Mark) Zhang, Philip S. Yu. Overlapping community detection combining content and link[J]. Journal of Zhejiang University Science C, 2012, 13(11): 828-839.

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doi="10.1631/jzus.C1200049"
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%A Zhong-fei (Mark) Zhang
%A Philip S. Yu
%J Journal of Zhejiang University SCIENCE C
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%N 11
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%DOI 10.1631/jzus.C1200049

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T1 - Overlapping community detection combining content and link
A1 - Zhou-zhou He
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A1 - Philip S. Yu
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EP - 839
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200049


Abstract: 
In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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