# Estimating Network Models using Neural Networks

arXiv: https://arxiv.org/abs/2502.01810 · HTML: https://mlsanity.com/paper/2502.01810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Neural Posterior Estimation on Exponential Random Graph Models: Evaluating Bias and Implementation Challenges](https://mlsanity.com/paper/2504.09349/) (2025)

## Abstract (arXiv)

Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitigate model misspecification. Some simple illustrative examples show that the method performs well in practice.