Turing patterns on time-varying networks
Reaction-diffusion systems evolving on complex networks can produce self-organized patterns. At variance with the common assumption, we investigate the case that the network itself is a dynamical entity. In the case of a periodically varying network, we show that the emergence of patterns can be predicted based on the averaged network. The condition is that the rewiring of the links is sufficiently fast. We compute an exact threshold for the corresponding fast-variation parameter, both in the discrete switching case, and in the case of continuously varying topologies.