Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Autoregressive (GNAR) models provide a parsimonious framework for exploiting the underlying network, even in the high-dimensional setting. We extend the GNAR framework by presenting the $\textit{community}$-$\alpha$ GNAR model that exploits prior knowledge and/or exogenous variables for identifying and modelling dynamic interactions across communities in the network. We further analyse the dynamics of $\textit{ Red, Blue}$ and $\textit{Swing}$ states throughout presidential elections in the USA. Our analysis suggests interesting global and communal effects.

翻译：网络时间序列在具有已知或推断底层网络结构的动态过程研究中正变得日益重要。广义网络自回归（GNAR）模型为利用底层网络提供了一个简约的框架，即使在高维环境下也适用。我们通过提出$\textit{社区}$-$\alpha$ GNAR模型扩展了GNAR框架，该模型利用先验知识和/或外生变量来识别和建模网络中跨社区的动态交互。我们进一步分析了美国大选中$\textit{红州}$、$\textit{蓝州}$和$\textit{摇摆州}$的动态变化。我们的分析揭示了有趣的全局效应和社区效应。