  Complex networks are the subject of fundamental interest from the scientific
community at large. Several metrics have been introduced to characterize the
structure of these networks, such as the degree distribution, degree
correlation, path length, clustering coefficient, centrality measures etc.
Another important feature is the presence of network symmetries. In particular,
the effect of these symmetries has been studied in the context of network
synchronization, where they have been used to predict the emergence and
stability of cluster synchronous states. Here we provide theoretical,
numerical, and experimental evidence that network symmetries play a role in a
substantially broader class of dynamical models on networks, including
epidemics, game theory, communication, and coupled excitable systems. Namely,
we see that in all these models, nodes that are related by a symmetry relation
show the same time-averaged dynamical properties. This discovery leads us to
propose reduction techniques for exact, yet minimal, simulation of complex
networks dynamics, which we show are effective in order to optimize the use of
computational resources, such as computation time and memory.
