Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding

10/02/2013

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by   Vince Lyzinski, et al.

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Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research. In thispaper, 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.