Font Size: 

\documentclass[graybox]{IFCS2024}


\usepackage{type1cm}        % activate if the above 3 fonts are
% not available on your system
%
\usepackage{makeidx}         % allows index generation
\usepackage{graphicx}        % standard LaTeX graphics tool
% when including figure files
\usepackage{multicol}        % used for the two-column index
\usepackage[bottom]{footmisc}% places footnotes at page bottom


\usepackage{newtxtext}       %
\usepackage[varvw]{newtxmath}       % selects Times Roman as basic font

% DO NOT USE OTHER COMMANDS OR MACROS


\makeindex

\begin{document}

\title*{Modelling clusters in network time series with an application to presidential elections in the USA \protect\linebreak
Submitted to IFCS 2024 Proceedings }
\titlerunning{Modelling clusters in network time series} %for an abbreviated version of your contribution title if the original one is too long
\author{Guy Nason, Daniel Salnikov and Mario Cortina-Borja}
\authorrunning{D. Salnikov et al.} %If there are more than two authors, please, abbreviate the authors' list using 'et al'

\institute{Guy Nason \at Imperial College London, Dept.\ Mathematics, Huxley Building, Imperial College, 180 Queen's Gate, South Kensington, London, SW7 2AZ, UK, \email{g.nason@imperial.ac.uk}
\and Daniel Salnikov \at Imperial College London, Dept.\ Mathematics, Huxley Building, Imperial College, 180 Queen's Gate, South Kensington, London, SW7 2AZ, UK, \email{d.salnikov22@imperial.ac.uk}
\and Mario Cortina-Borja \at University College London, Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK, \email{m.cortina@ucl.ac.uk}
}


\maketitle

\abstract*{
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 \mbox{\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 {\em Red, Blue} and \textit{Swing} states throughout presidential elections in the USA. Our analysis shows that dynamics differ among the state-wise clusters.
}

\keywords{time series clustering, Generalised Network Autoregressive (GNAR) process, community interactions,
Wagner plot}

\end{document}

time series clustering, Generalised Network Autoregressive (GNAR) process, community interactions, Wagner plot