A type of dynamic network involves temporally ordered interactions between actors, where past network configurations may influence future ones. The relational event model can be used to identify the underlying dynamics that drive interactions among system components. Despite the rapid development of this model over the past 15 years, an ongoing area of research revolves around evaluating the goodness of fit of this model, especially when it incorporates time-varying and random effects. Current methodologies often rely on comparing observed and simulated events using specific statistics, but this can be computationally intensive, and requires various assumptions. We propose an additive mixed-effect relational event model estimated via case-control sampling, and introduce a versatile framework for testing the goodness of fit of such models using weighted martingale residuals. Our focus is on a Kolmogorov-Smirnov type test designed to assess if covariates are accurately modeled. Our approach can be easily extended to evaluate whether other features of network dynamics have been appropriately incorporated into the model. We assess the goodness of fit of various relational event models using synthetic data to evaluate the test's power and coverage. Furthermore, we apply the method to a social study involving 57,791 emails sent by 159 employees of a Polish manufacturing company in 2010. The method is implemented in the R package mgcv.

翻译：一类动态网络涉及参与者之间按时间顺序排列的交互，其中过去的网络配置可能影响未来的交互。关系事件模型可用于识别驱动系统组件间交互的潜在动态机制。尽管该模型在过去15年间发展迅速，但如何评估其拟合优度——尤其是在纳入时变效应与随机效应时——始终是一个持续的研究方向。现有方法通常依赖特定统计量比较观测事件与模拟事件，但这种方法计算成本高昂且需要多种假设。我们提出一种通过病例对照抽样估计的加性混合效应关系事件模型，并引入一个基于加权鞅残差的通用框架来检验此类模型的拟合优度。我们重点构建了Kolmogorov-Smirnov型检验，用于评估协变量是否被准确建模。该方法可轻松扩展至检验网络动态的其他特征是否被恰当纳入模型。我们使用合成数据评估了多种关系事件模型的拟合优度，以检验所提方法的功效与覆盖概率。此外，我们将该方法应用于一项社会研究，该研究涉及波兰某制造企业159名员工在2010年发送的57,791封电子邮件。本方法已通过R软件包mgcv实现。