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

  • Teague R. HenryEmail author
  • Kathleen M. Gates
  • Mitchell J. Prinstein
  • Douglas Steinley
Original Research


This article develops a class of models called sender/receiver finite mixture exponential random graph models (SRFM-ERGMs). 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 without a block structure. 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 unobserved heterogeneity in effects of nodal covariates can be a major cause of misfit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.


p* exponential random graphs finite mixture modeling individual differences modeling 



Funding was provided by National Science Foundation (US) (DGE-1650116) and National Institute on Alcohol Abuse and Alcoholism (US) (Grant No. 1R21AA022074).

Supplementary material

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Copyright information

© The Psychometric Society 2019

Authors and Affiliations

  1. 1.University of North Carolina at Chapel HillChapel HillUSA
  2. 2.University of MissouriColumbiaUSA

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