Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models
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.
Keywordsp* 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).
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