This paper explores time heterogeneity in stochastic actor oriented models (SAOM) proposed by Snijders (Sociological methodology. Blackwell, Boston, pp 361–395, 2001) which are meant to study the evolution of networks. SAOMs model social networks as directed graphs with nodes representing people, organizations, etc., and dichotomous relations representing underlying relationships of friendship, advice, etc. We illustrate several reasons why heterogeneity should be statistically tested and provide a fast, convenient method for assessment and model correction. SAOMs provide a flexible framework for network dynamics which allow a researcher to test selection, influence, behavioral, and structural properties in network data over time. We show how the forward-selecting, score type test proposed by Schweinberger (Chapter 4: Statistical modeling of network panel data: goodness of fit. PhD thesis, University of Groningen 2007) can be employed to quickly assess heterogeneity at almost no additional computational cost. One step estimates are used to assess the magnitude of the heterogeneity. Simulation studies are conducted to support the validity of this approach. The ASSIST dataset (Campbell et al. In Lancet 371(9624):1595–1602, 2008) is reanalyzed with the score type test, one step estimators, and a full estimation for illustration. These tools are implemented in the RSiena package, and a brief walkthrough is provided.

中文翻译：

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评估和解释随机演员导向模型中的时间异质性。

本文探讨了 Snijders（社会学方法论。布莱克威尔，波士顿，第 361-395 页，2001 年）提出的随机演员导向模型 (SAOM) 中的时间异质性，该模型旨在研究网络的演化。SAOM 将社交网络建模为有向图，节点代表人、组织等，二分关系代表友谊、建议等的潜在关系。我们说明了为什么应该对异质性进行统计测试的几个原因，并提供一种快速、方便的评估和评估方法。模型修正。SAOM 为网络动力学提供了一个灵活的框架，允许研究人员随着时间的推移测试网络数据中的选择、影响、行为和结构特性。我们展示了 Schweinberger 提出的前向选择、评分类型测试（第 4 章：网络面板数据的统计建模：拟合优度。博士论文，格罗宁根大学 2007 年）可用于快速评估异质性，几乎不需要额外的计算成本。一步估计用于评估异质性的大小。进行模拟研究以支持这种方法的有效性。ASSIST 数据集（Campbell et al. In Lancet 371(9624):1595–1602, 2008）通过分数类型测试、一步估计和完整估计进行重新分析以供说明。这些工具在 RSiena 包中实现，并提供了一个简短的演练。进行模拟研究以支持这种方法的有效性。ASSIST 数据集（Campbell et al. In Lancet 371(9624):1595–1602, 2008）通过分数类型测试、一步估计和完整估计进行重新分析以供说明。这些工具在 RSiena 包中实现，并提供了一个简短的演练。进行模拟研究以支持这种方法的有效性。ASSIST 数据集（Campbell et al. In Lancet 371(9624):1595–1602, 2008）通过分数类型测试、一步估计和完整估计进行重新分析以供说明。这些工具在 RSiena 包中实现，并提供了一个简短的演练。