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 introducing the $\textit{community}$-$\alpha$ GNAR model that exploits prior knowledge and/or exogenous variables for identifying and modelling dynamic interactions across communities in the underlying network. We further analyse the dynamics of $\textit{Red, Blue}$ and $\textit{Swing}$ states throughout presidential elections in the USA. Our analysis shows that dynamics differ among the state-wise clusters.

翻译：网络时间序列在研究具有已知或推断出的基础网络结构的动态过程中日益重要。广义网络自回归模型提供了一种简洁的框架，即使在数据高维度的情境下也能有效利用基础网络。我们扩展了GNAR框架，引入了$\textit{社区}$-$\alpha$ GNAR模型，该模型利用先验知识和/或外生变量，以识别并建模基础网络中跨社区的动态交互作用。我们进一步分析了美国总统选举中$\textit{红州}$、$\textit{蓝州}$和$\textit{摇摆州}$的动态变化。分析表明，各州集群之间的动态特征存在差异。