Second-order Approximation of Exponential Random Graph Models

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Ding, Wen-Yi, Fang, Xiao
Formato: Preprint
Publicado: 2024
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866910286185758720
author Ding, Wen-Yi
Fang, Xiao
author_facet Ding, Wen-Yi
Fang, Xiao
contents Exponential random graph models (ERGMs) are flexible probability models allowing edge dependency. However, it is known that, to a first-order approximation, many ERGMs behave like Erdös-Rényi random graphs, where edges are independent. In this paper, to distinguish ERGMs from Erdös-Rényi random graphs, we consider second-order approximations of ERGMs using two-stars and triangles. We prove that the second-order approximation indeed achieves second-order accuracy in the triangle-free case. The new approximation is formally obtained by Hoeffding decomposition and rigorously justified using Stein's method.
format Preprint
id arxiv_https___arxiv_org_abs_2401_01467
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Second-order Approximation of Exponential Random Graph Models
Ding, Wen-Yi
Fang, Xiao
Probability
Exponential random graph models (ERGMs) are flexible probability models allowing edge dependency. However, it is known that, to a first-order approximation, many ERGMs behave like Erdös-Rényi random graphs, where edges are independent. In this paper, to distinguish ERGMs from Erdös-Rényi random graphs, we consider second-order approximations of ERGMs using two-stars and triangles. We prove that the second-order approximation indeed achieves second-order accuracy in the triangle-free case. The new approximation is formally obtained by Hoeffding decomposition and rigorously justified using Stein's method.
title Second-order Approximation of Exponential Random Graph Models
topic Probability
url https://arxiv.org/abs/2401.01467