We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

中文翻译：

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动态网络预测

我们提出了一个统计框架，用于根据观察到的人群中社会关系的演变来生成预测的动态网络。该框架包括一个新颖而灵活的过程，可以根据不断变化的网络属性对动态网络进行采样；它允许使用广泛的方法来模拟趋势、季节性变化、不确定性和人口构成的变化。当前的方法在预测网络结构时没有考虑到观察到的历史网络的可变性；所提出的方法提供了一种将不确定性纳入预测的原则方法。这一进步有助于设计基于网络的干预措施，因为此类干预措施的开发通常需要预测干预措施存在和不存在时的网络结构。进行了两项模拟研究以证明在设计基于网络的干预措施时生成预测网络的有用性。该框架还通过使用动态网络调查对法案通过率的潜在干预结果进行了说明，该网络代表参议员之间的发起人/共同发起人关系，这些关系源自 2003 年至 2016 年美国参议院提出的法案。