Dynamics of interacting information waves in networks

Abstract

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 analytical calculations of the scaling for a one-dimensional system.

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

“…Random walk based approaches are also highly popular. For instance, random walks are used to model the interaction of "information waves" in a graph [47], which is useful to describe at the same time the spread and the interaction of information over time. Analogously, the concepts of hitting (average probability that two random walkers are in the same state at the same time) and commute time (average return time to the initial state) in random walks have been used by Qiu and Hancock [57] to characterize graphs for pattern recognition purpose.…”