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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (11): 828-839    DOI: 10.1631/jzus.C1200049
    
Overlapping community detection combining content and link
Zhou-zhou He, Zhong-fei (Mark) Zhang, Philip S. Yu
Zhejiang Provincial Key Laboratory of Information Network Technology, Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; Department of Computer Science, University of Illinois at Chicago, IL 60607, USA
Overlapping community detection combining content and link
Zhou-zhou He, Zhong-fei (Mark) Zhang, Philip S. Yu
Zhejiang Provincial Key Laboratory of Information Network Technology, Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; Department of Computer Science, University of Illinois at Chicago, IL 60607, USA
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摘要： 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.
关键词： Overlapping;  Content;  Link;  Community detection    
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.
Key words: Overlapping    Content    Link    Community detection
收稿日期: 2012-03-05 出版日期: 2012-11-02
CLC:  TP391  
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Zhou-zhou He
Zhong-fei (Mark) Zhang
Philip S. Yu

引用本文:

Zhou-zhou He, Zhong-fei (Mark) Zhang, Philip S. Yu. Overlapping community detection combining content and link. Front. Inform. Technol. Electron. Eng., 2012, 13(11): 828-839.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200049    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I11/828

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