文章基本信息

标题：Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
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作者：Vince Lyzinski ; Daniel L. Sussman ; Minh Tang 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2014
卷号：8
期号：2
页码：2905-2922
DOI：10.1214/14-EJS978
语种：English
出版社：Institute of Mathematical Statistics
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.