Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding

被引：61

作者：

Lyzinski, Vince ^{[1
]}

Sussman, Daniel L. ^{[2
]}

Minh Tang ^{[3
]}

Athreya, Avanti ^{[3
]}

Priebe, Carey E. ^{[3
]}

机构：

[1] Johns Hopkins Univ, Human Language Technol Ctr Excellence, Baltimore, MD 21211 USA

[2] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA

[3] Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD 21218 USA

来源：

ELECTRONIC JOURNAL OF STATISTICS | 2014年 / 8卷

关键词：

Clustering; stochastic block model; degree corrected stochastic block model; VERTEX CLASSIFICATION; CONSISTENCY;

D O I：

10.1214/14-EJS978

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In this paper, we provide a short proof that the adjacency spectral embedding can be used to obtain perfect clustering for the stochastic blockmodel and the degree-corrected stochastic blockmodel. We also show an analogous result for the more general random dot product graph model.

引用

页码：2905 / 2922

页数：18

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