An Improved Spectral Clustering Algorithm for Community Discovery

摘要：

For discovering communities in social network, an improved spectral clustering method is presented in this paper. To make full use of the network feature, the core members are used in this method for mining communities. This goal has been achieved through the Page Rank method, which is common in directed graphs, for the reason that an undirected graph can be treated as the special case of the corresponding directed one. Following that, they can be used for initialization in the spectral clustering to avoid the sensitivity to the initial centroids. Applied to four datasets, the improved method turns out to be better than the traditional spectral clustering methods, whether in time or in accuracy aspect.

关键词： community discovery spectral clustering core member Page Rank

作者: Shuzi Niu Daling Wang Shi Feng Ge Yu

作者单位: School of Information Science and Engineering, Northeastern University Shenyang, P.R.China Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education Shenyang,

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）