Abstract Changes in computation and automated data collection have greatly increased interest in statistical models of dynamic networks. Many of the models employed for inference on large-scale dynamic networks suffer from limited forward simulation/prediction capabilities. One major problem with many of the forward simulation procedures is a tendency for the model to become degenerate in only a few time steps, that is, the simulation/prediction procedure results in either null graphs or complete graphs. Here, we describe an algorithm for simulating a sequence of networks generated from lagged dynamic network regression models DNR(V), a subfamily of TERGMs. Further, we introduce a smoothed estimator for forward prediction based on smoothing of the change statistics obtained for a dynamic network regression model. We focus on the implementation of the algorithm, providing a series of motivating examples with comparisons to dynamic network models from the literature. We find that our algorithm significantly improves multistep prediction/simulation over standard DNR(V) forecasting. Furthermore, we show that our method performs comparably to existing more complex dynamic network analysis frameworks (SAOM and STERGMs) for small networks over short time periods, and significantly outperforms these approaches over long time time intervals and/or large networks. Supplementary materials for this article are available online.

中文翻译：

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来自滞后动态网络回归模型的稳定多时间步模拟/预测*

摘要 计算和自动化数据收集的变化极大地增加了对动态网络统计模型的兴趣。许多用于大规模动态网络推理的模型都受到有限的前向模拟/预测能力的影响。许多前向模拟程序的一个主要问题是模型在仅几个时间步长内就变得退化的趋势，即模拟/预测程序导致空图或完整图。在这里，我们描述了一种算法，用于模拟从滞后动态网络回归模型 DNR(V)（TERGM 的一个子族）生成的一系列网络。此外，我们基于对动态网络回归模型获得的变化统计的平滑引入了用于前向预测的平滑估计器。我们专注于算法的实现，提供了一系列与文献中动态网络模型进行比较的激励示例。我们发现我们的算法比标准 DNR(V) 预测显着改进了多步预测/模拟。此外，我们表明我们的方法在短时间内与现有的更复杂的动态网络分析框架（SAOM 和 STERGMs）在小型网络上的表现相当，并且在长时间间隔和/或大型网络上显着优于这些方法。本文的补充材料可在线获取。此外，我们表明我们的方法在短时间内与现有的更复杂的动态网络分析框架（SAOM 和 STERGMs）在小型网络上的表现相当，并且在长时间间隔和/或大型网络上显着优于这些方法。本文的补充材料可在线获取。此外，我们表明我们的方法在短时间内与现有的更复杂的动态网络分析框架（SAOM 和 STERGMs）在小型网络上的表现相当，并且在长时间间隔和/或大型网络上显着优于这些方法。本文的补充材料可在线获取。