Modeling Heterogeneous Peer Assortment Effects using Finite Mixture Exponential Random Graph Models

TL;DR

This paper introduces SRFM-ERGMs, a new modeling approach that captures unobserved heterogeneity in network effects, improving fit and inference in social network analysis, demonstrated through adolescent substance use data.

Contribution

The paper develops SRFM-ERGMs, extending ERGMs to account for unobserved heterogeneity, enhancing network modeling accuracy.

Findings

Unobserved heterogeneity significantly affects network model fit.

SRFM-ERGMs improve inference accuracy in social network analysis.

Method effectively captures heterogeneity in empirical data.

Abstract

This article develops a class of models called Sender/Receiver Finite Mixture Exponential Random Graph Models (SRFM-ERGMs) that enables inference on networks. This class of models extends the existing Exponential Random Graph Modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that that unobserved heterogeneity in effects of nodal covariates can be a major cause of mis-fit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.