Pseudo-likelihood-based $M$-estimation of random graphs with dependent edges and parameter vectors of increasing dimension

TL;DR

This paper develops scalable pseudo-likelihood-based $M$-estimators for dependent random graph models with increasing parameter dimensions, addressing computational and statistical challenges in network data analysis.

Contribution

It introduces a novel class of generalized $eta$-models with dependent edges and provides convergence rate analysis for these models, considering complex phenomena like phase transitions.

Findings

Established convergence rates for pseudo-likelihood estimators in dependent graph models.

Demonstrated applicability to dense and sparse network settings.

Highlighted the impact of phase transitions and near-degeneracy on estimation accuracy.

Abstract

An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing computational scalability and statistical guarantees. We demonstrate that scalable estimation of random graph models with dependent edges is possible, by establishing convergence rates of pseudo-likelihood-based $M$-estimators for discrete undirected graphical models with exponential parameterizations and parameter vectors of increasing dimension in single-observation scenarios. We highlight the impact of two complex phenomena on the convergence rate: phase transitions and model near-degeneracy. The main results have possible applications to discrete and dependent network, spatial, and temporal data. To showcase convergence rates, we introduce a novel class of generalized $\beta$-models with dependent edges and parameter vectors of increasing dimension, which leverage additional structure in the form of overlapping subpopulations to control dependence. We establish convergence rates of pseudo-likelihood-based $M$-estimators for generalized $\beta$-models in dense- and sparse-graph settings.