Overlapping community detection combining content and link

被引:0
作者
Zhou-zhou He
Zhong-fei Mark Zhang
Philip S. Yu
机构
[1] Zhejiang University,Zhejiang Provincial Key Laboratory of Information Network Technology, Department of Information Science and Electronic Engineering
[2] University of Illinois at Chicago,Department of Computer Science
来源
Journal of Zhejiang University SCIENCE C | 2012年 / 13卷
关键词
Overlapping; Content; Link; Community detection; TP391;
D O I
暂无
中图分类号
学科分类号
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.
引用
收藏
页码:828 / 839
页数:11
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