  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.
