Bayesian Testing of Scientific Expectations under Exponential Random Graph Models

Joris Mulder, Nial Friel, Philip Leifeld

Research output: Contribution to journalArticlepeer-review

Abstract

The exponential random graph (ERGM) model is a commonly used statistical framework for studying the determinants of tie formations from social network data. To test scientific theories under ERGMs, statistical inferential techniques are generally used based on traditional significance testing using p-values. This methodology has certain limitations, however, such as its inconsistent behavior when the null hypothesis is true, its inability to quantify evidence in favor of a null hypothesis, and its inability to test multiple hypotheses with competing equality and/or order constraints on the parameters of interest in a direct manner. To tackle these shortcomings, this paper presents Bayes factors and posterior probabilities for testing scientific expectations under a Bayesian framework. The methodology is implemented in the R package BFpack. The applicability of the methodology is illustrated using empirical collaboration networks and policy networks.
Original languageUndefined
Pages (from-to)40-53
JournalSocial Networks
Volume78
DOIs
Publication statusAccepted/In press - 2 Dec 2023

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