  The statistical field theory of information dynamics on complex networks
concerns the dynamical evolution of large classes of models of complex systems.
Previous work has focused on networks where nodes carry an information field,
which describes the internal state of the node, and its dynamical evolution. In
this work, we propose a more general mathematical framework to model
information dynamics on complex networks, where the internal node states are
vector valued, thus allowing each node to carry multiple types of information.
This setup is relevant for many biophysical and socio-technological models of
complex systems, ranging from viral dynamics on networks to models of opinion
dynamics and social contagion. The full dynamics of these systems is described
in the space of all possible network configurations, as opposed to a node-based
perspective. Here, we illustrate the mathematical framework presented in an
accompanying letter, while focusing on an exemplary application of epidemic
spreading on a low-dimensional network.
