Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Machine Learning
2015-01-19 v4
Abstract
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.
Keywords
Cite
@article{arxiv.1310.0532,
title = {Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding},
author = {Vince Lyzinski and Daniel Sussman and Minh Tang and Avanti Athreya and Carey Priebe},
journal= {arXiv preprint arXiv:1310.0532},
year = {2015}
}
Comments
22 pages, including references; 2 figures