BibTeX

@article{2504.09349v1,
Author        = {Yefeng Fan and Simon White},
Title         = {Neural Posterior Estimation on Exponential Random Graph Models:
  Evaluating Bias and Implementation Challenges},
Eprint        = {2504.09349v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Exponential random graph models (ERGMs) are flexible probabilistic frameworks
to model statistical networks through a variety of network summary statistics.
Conventional Bayesian estimation for ERGMs involves iteratively exchanging with
an auxiliary variable due to the intractability of ERGMs, however, this
approach lacks scalability to large-scale implementations. Neural posterior
estimation (NPE) is a recent advancement in simulation-based inference, using a
neural network based density estimator to infer the posterior for models with
doubly intractable likelihoods for which simulations can be generated. While
NPE has been successfully adopted in various fields such as cosmology, little
research has investigated its use for ERGMs. Performing NPE on ERGM not only
provides a differing angle of resolving estimation for the intractable ERGM
likelihoods but also allows more efficient and scalable inference using the
amortisation properties of NPE, and therefore, we investigate how NPE can be
effectively implemented in ERGMs.
  In this study, we present the first systematic implementation of NPE for
ERGMs, rigorously evaluating potential biases, interpreting the biases
magnitudes, and comparing NPE fittings against conventional Bayesian ERGM
fittings. More importantly, our work highlights ERGM-specific areas that may
impose particular challenges for the adoption of NPE.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.09349v1},
File          = {2504.09349v1.pdf}
}