Large deviations and exact asymptotics for constrained exponential random graphs

TL;DR

The paper introduces a technique for approximating normalization constants under constraints and applies it to derive precise asymptotics for constrained exponential random graphs.

Contribution

It provides a novel method for exact asymptotics of normalization constants in constrained exponential random graph models.

Findings

Derived exact asymptotics for constrained exponential random graphs.

Developed a general technique for approximating normalization constants with constraints.

Enhanced understanding of large deviations in exponential random graph models.

Abstract

We present a technique for approximating generic normalization constants subject to constraints. The method is then applied to derive the exact asymptotics for the conditional normalization constant of constrained exponential random graphs.