University of North Carolina at Chapel Hill, Chapel Hill, USA.
University of Missouri, Columbia, USA.
Psychometrika. 2020 Mar;85(1):8-34. doi: 10.1007/s11336-019-09685-2. Epub 2019 Aug 26.
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.
本文提出了一类称为发送方/接收方有限混合指数随机图模型（SRFM-ERGMs）的模型。该类模型扩展了现有的指数随机图建模框架，使分析师能够在没有块结构的情况下对节点协变量和网络特征的影响中的未观测异质性进行建模。本文提供了一个关于青少年物质使用的实证示例。通过各种条件下的模拟来评估该技术的性能。我们的结论是，节点协变量效应中的未观测异质性可能是网络模型拟合不良的一个主要原因，而 SRFM-ERGM 方法可以缓解这种不匹配。最后讨论了该方法对心理学科学中社会网络分析的意义。
