Efficient inference in stochastic block models with vertex labels
Machine Learning
2019-05-24 v2 Machine Learning
Probability
Abstract
We study the stochastic block model with two communities where vertices contain side information in the form of a vertex label. These vertex labels may have arbitrary label distributions, depending on the community memberships. We analyze a linearized version of the popular belief propagation algorithm. We show that this algorithm achieves the highest accuracy possible whenever a certain function of the network parameters has a unique fixed point. Whenever this function has multiple fixed points, the belief propagation algorithm may not perform optimally. We show that increasing the information in the vertex labels may reduce the number of fixed points and hence lead to optimality of belief propagation.
Keywords
Cite
@article{arxiv.1806.07562,
title = {Efficient inference in stochastic block models with vertex labels},
author = {Clara Stegehuis and Laurent Massoulié},
journal= {arXiv preprint arXiv:1806.07562},
year = {2019}
}