Lospinoso Joshua A, Schweinberger Michael, Snijders Tom A B, Ripley Ruth M
Department of Statistics, University of Oxford, Oxford, UK. Network Science Center, United States Military Academy, New York, USA.
Adv Data Anal Classif. 2011 Jul;5(2):147-176. doi: 10.1007/s11634-010-0076-1.
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. 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.
本文探讨了由斯尼德斯（《社会学方法》。布莱克韦尔出版社，波士顿，第361 - 395页，2001年）提出的随机行为者导向模型（SAOM）中的时间异质性，该模型旨在研究网络的演化。SAOM将社会网络建模为有向图，其中节点代表人、组织等，二分关系代表潜在的友谊、建议等关系。我们阐述了几个为何应进行异质性统计检验的原因，并提供了一种快速、便捷的评估和模型校正方法。SAOM为网络动态提供了一个灵活的框架，使研究人员能够随时间检验网络数据中的选择、影响、行为和结构属性。我们展示了如何采用施温伯格（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）提出的前向选择评分型检验，以几乎不增加额外计算成本的方式快速评估异质性。单步估计用于评估异质性的大小。进行了模拟研究以支持该方法的有效性。为作说明，使用评分型检验、单步估计器和全估计对ASSIST数据集（坎贝尔等人，《柳叶刀》371(9624):1595 - 1602，2008年）进行了重新分析。这些工具在RSiena软件包中实现，并提供了简要的操作指南。
