Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model

TL;DR

This paper proves the identifiability and consistency of maximum-likelihood and variational estimators in the stochastic block model, advancing understanding of asymptotic inference in network analysis.

Contribution

It establishes the first consistency results for variational estimators in the context of random graph models like SBM.

Findings

Identifiability of SBM proved

Asymptotic properties of estimators analyzed

Consistency of variational estimators established

Abstract

The stochastic block model (SBM) is a probabilistic model de- signed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference on SBM by use of maximum- likelihood and variational approaches. The identi ability of SBM is proved, while asymptotic properties of maximum-likelihood and variational esti- mators are provided. In particular, the consistency of these estimators is settled, which is, to the best of our knowledge, the rst result of this type for variational estimators with random graphs.