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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
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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 wordsOverlapping      Content      Link      Community detection     
Received: 05 March 2012      Published: 02 November 2012
CLC:  TP391  
Cite this article:

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


Overlapping community detection combining content and link

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 
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