Simulation-Based Estimation of General Structural Network Models


This paper addresses the issue of estimating structural graph/network models on data from a single graph/network. While there exist methods for estimating structural models on many independent and identically distributed (iid) graphs/networks (Banerjee et. al., 2013), there are limited general-purpose algorithms to fit structural models on a given single network. With most graphs/networks, the likelihood function is both intractable, and graphs/networks can't easily be split into many iid components, so traditional methods like maximum likelihood and method of moments will not work. This study proposes an algorithm adapted from Deep Learning, dubbed Sequential Neural Posterior Estimation (SNPE), for network analysis. SNPE is a simulation-based estimator that can estimate likelihoods without a likelihood function and does not require a cross-section of iid samples. These two facts allow for general-purpose estimation of structural network/graph models. SNPE can recover calibrated parameter values that generate a ground truth graph/network from a structural model. Additionally, the algorithm is applied to estimate the homophily model (Bramoulle et. al. 2012) on empirical data which was not attempted in the original paper. This study presents a promising algorithm for fitting structural models on a single network, opening avenues for future research in network estimation.