Regular Decomposition: an information and graph theoretic approach to stochastic block models
Information Theory
2019-08-14 v5 math.IT
Abstract
A method for compression of large graphs and non-negative matrices to a block structure is proposed. Szemer\'edi's regularity lemma is used as heuristic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We propose practical algorithms and provide theoretical results on the accuracy of the method.
Cite
@article{arxiv.1704.07114,
title = {Regular Decomposition: an information and graph theoretic approach to stochastic block models},
author = {Hannu Reittu and Fülöp Bazsó and Ilkka Norros},
journal= {arXiv preprint arXiv:1704.07114},
year = {2019}
}
Comments
Simulation example added. Poisson block model code length estimates changed