Assessing variational likelihood estimation in network formation models
- 1Department of Statistics & Data Science, University of California - Los Angeles, Los Angeles, California, United States of America
Abstract
Networks are often a useful way to represent complex social and economic systems. Stochastic models for such networks should parsimoniously reflect the complexity while being both interpretable and tractable. The class of Exponential-family Random Graph Models (ERGMs) is very expressive and flexible in its representation of complex phenomena. However, the estimation and fitting of realistic ERGMs are hampered by the computational challenges of likelihood-based inference. Recently, a deterministic method based on a variational mean-field approximation was proposed that is much less expensive to compute than the standard simulation-based method. In this work, we show that the proposed method performs poorly for realistic models. We show that established alternatives, including MCMC-MLE, MPLE, sampled MPLE, and tapered ERGM-based inference, achieve superior accuracy and computational performance in the situations that the mean-field approximation was designed for.
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BibTeX
@article{resch2026assessing,
title = {{Assessing variational likelihood estimation in network formation models}},
author = {Joseph Resch and Mark S. Handcock},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358535},
url = {https://doi.org/10.1371/journal.pone.0358535},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Assessing variational likelihood estimation in network formation models
AU - Joseph Resch
AU - Mark S. Handcock
PY - 2026
DA - 2026/10/06
JO - PLOS One
DO - 10.1371/journal.pone.0358535
UR - https://doi.org/10.1371/journal.pone.0358535
AB - Networks are often a useful way to represent complex social and economic systems. Stochastic models for such networks should parsimoniously reflect the complexity while being both interpretable and tractable. The class of Exponential-family Random Graph Models (ERGMs) is very expressive and flexible in its representation of complex phenomena. However, the estimation and fitting of realistic ERGMs are hampered by the computational challenges of likelihood-based inference. Recently, a deterministic method based on a variational mean-field approximation was proposed that is much less expensive to compute than the standard simulation-based method. In this work, we show that the proposed method performs poorly for realistic models. We show that established alternatives, including MCMC-MLE, MPLE, sampled MPLE, and tapered ERGM-based inference, achieve superior accuracy and computational performance in the situations that the mean-field approximation was designed for.
SN - 1932-6203
ER -