We assess the reliability of relational event model (REM) parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case–control sampling which samples nonevents, or null dyads (“controls”), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that REMs can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of some tens of thousands of events and a small number of controls per event. Using the data that we collected on the Wikipedia editing network, we illustrate how network effects commonly included in empirical studies based on REMs need widely different sample sizes to be reliably estimated. For our analysis we use an open-source software which implements the two sampling schemes, allowing analysts to fit and analyze REMs to the same or other data that may be collected in different empirical settings, varying sample parameters or model specification.

中文翻译：

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抽样下关系事件模型估计的可靠性：如何将关系事件模型拟合到 3.6 亿个二元事件

我们评估了在两种抽样方案下估计的关系事件模型 (REM) 参数的可靠性：(1) 从观察到的事件中进行统一抽样；(2) 从适当定义的风险集。我们通过实验分别确定估计参数的可变性作为采样事件数和每个事件控制数的函数。结果表明，通过分析数万个事件的样本和每个事件的少量控制，REM 可以可靠地拟合到由超过 3.6 亿个二元事件连接的超过 1200 万个节点的网络。使用我们在维基百科编辑网络上收集的数据，我们说明了基于 REM 的实证研究中通常包含的网络效应如何需要广泛不同的样本量才能被可靠地估计。在我们的分析中，我们使用了一个开源软件，它实现了两种采样方案，允许分析师将 REM 拟合和分析到相同或其他数据，这些数据可能在不同的经验设置、不同的样本参数或模型规范中收集。