  To better understand the inner workings of information spreading, network
researchers often use simple models to capture the spreading dynamics. But most
models only highlight the effect of local interactions on the global spreading
of a single information wave, and ignore the effects of interactions between
multiple waves. Here we take into account the effect of multiple interacting
waves by using an agent-based model in which the interaction between
information waves is based on their novelty. We analyzed the global effects of
such interactions and found that information that actually reaches nodes
reaches them faster. This effect is caused by selection between information
waves: slow waves die out and only fast waves survive. As a result, and in
contrast to models with non-interacting information dynamics, the access to
information decays with the distance from the source. Moreover, when we
analyzed the model on various synthetic and real spatial road networks, we
found that the decay rate also depends on the path redundancy and the effective
dimension of the system. In general, the decay of the information wave
frequency as a function of distance from the source follows a power law
distribution with an exponent between -0.2 for a two-dimensional system with
high path redundancy and -0.5 for a tree-like system with no path redundancy.
We found that the real spatial networks provide an infrastructure for
information spreading that lies in between these two extremes. Finally, to
better understand the mechanics behind the scaling results, we provide analytic
calculations of the scaling for a one-dimensional system.
