Substantive implications of unobserved heterogeneity: Testing the frailty approach to exponential random graph models

Abstract

Exponential Random Graph Models (ERGMs) are an increasingly common tool for inferential network analysis. However, a potential problem for these models is the assumption of correct model specification. Through six substantive applications (Mesa High, Florentine Marriage, Military Alliances, Militarized Interstate Disputes, Regional Planning, Brain Complexity), we illustrate how unobserved heterogeneity and confounding leads to degenerate model specifications, inferential errors, and poor model fit. In addition, we present evidence that a better approach exists in the form of the Frailty Exponential Random Graph Model (FERGM), which extends the ERGM to account for unit or group-level heterogeneity in tie formation. In each case, the ERGM is prone to producing inferential errors and forecasting ties with lower accuracy than the FERGM.