  Trophic coherence, a measure of the extent to which the nodes of a directed
network are organised in levels, has recently been shown to be closely related
to many structural and dynamical aspects of complex systems, including graph
eigenspectra, the prevalence or absence of feed-back cycles, and linear
stability. Furthermore, non-trivial trophic structures have been observed in
networks of neurons, species, genes, metabolites, cellular signalling,
concatenated words, P2P users, and world trade. Here we consider two simple yet
apparently quite different dynamical models -- one a
Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include
complex contagion, the other an Amari-Hopfield neural network -- and show that
in both cases the related spreading processes are modulated in similar ways by
the trophic coherence of the underlying networks. To do this, we propose a
network assembly model which can generate structures with tunable trophic
coherence, limiting in either perfectly stratified networks or random graphs.
We find that trophic coherence can exert a qualitative change in spreading
behaviour, determining whether a pulse of activity will percolate through the
entire network or remain confined to a subset of nodes, and whether such
activity will quickly die out or endure indefinitely. These results could be
important for our understanding of phenomena such as epidemics, rumours, shocks
to ecosystems, neuronal avalanches, and many other spreading processes.
