English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Network time series are becoming increasingly relevant in the study ofdynamic processes characterised by a known or inferred underlying networkstructure. Generalised Network Autoregressive (GNAR) models provide aparsimonious framework for exploiting the underlying network, even in thehigh-dimensional setting. We extend the GNAR framework by introducing the$\textit{community}$-$\alpha$ GNAR model that exploits prior knowledge and/orexogenous variables for identifying and modelling dynamic interactions acrosscommunities in the underlying network. We further analyse the dynamics of$\textit{Red, Blue}$ and $\textit{Swing}$ states throughout presidentialelections in the USA. Our analysis shows that dynamics differ among thestate-wise clusters.

Modelling clusters in network time series with an application to presidential elections in the USA