Open Access
2023 Likelihood-based inference for exponential-family random graph models via linear programming
Pavel N. Krivitsky, Alina R. Kuvelkar, David R. Hunter
Author Affiliations +
Electron. J. Statist. 17(2): 3337-3356 (2023). DOI: 10.1214/23-EJS2176


The problem of determining whether a given point, or set of points, lies within the convex hull of another set of points in d dimensions arises naturally in the context of certain exponential family models in statistics. This article discusses the general convex hull problem and its application to the particular problem of modelling network data using an exponential-family random graph model (ERGM). While the convex hull question may be solved via a simple linear program, this approach is not well known in the statistical literature. The article also details several substantial improvements to the convex hull-testing algorithm currently implemented in the widely used ergm package for network modeling. It provides direct numerical comparisons of two linear programming packages for R that can be called by ergm and offers several illustrative examples.

Funding Statement

The first author was supported in part by US Army Research Office Award W911NF-21-1-0335 (79034-NS).


The authors would like to thank the reviewers, the associate editor, and the editor, who all provided helpful feedback during the preparation of this paper.


Download Citation

Pavel N. Krivitsky. Alina R. Kuvelkar. David R. Hunter. "Likelihood-based inference for exponential-family random graph models via linear programming." Electron. J. Statist. 17 (2) 3337 - 3356, 2023.


Received: 1 February 2022; Published: 2023
First available in Project Euclid: 21 November 2023

arXiv: 2202.03572
Digital Object Identifier: 10.1214/23-EJS2176

Primary: 62-04 , 62-08
Secondary: 62F30

Keywords: Convex hull , Duality , MCMC , missing data

Vol.17 • No. 2 • 2023
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