Estimating Network Models using Neural Networks
Social and Information Networks
2025-02-05 v1 Econometrics
Computation
Machine Learning
Abstract
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.
Cite
@article{arxiv.2502.01810,
title = {Estimating Network Models using Neural Networks},
author = {Angelo Mele},
journal= {arXiv preprint arXiv:2502.01810},
year = {2025}
}