8 research outputs found
Reactive random walkers on complex networks
We introduce and study a metapopulation model of random walkers interacting
at the nodes of a complex network. The model integrates random relocation moves
over the links of the network with local interactions depending on the node
occupation probabilities. The model is highly versatile, as the motion of the
walkers can be fed on topological properties of the nodes, such as their
degree, while any general nonlinear function of the occupation probability of a
node can be considered as local reaction term. In addition to this, the
relative strength of reaction and relocation can be tuned at will, depending on
the specific application being examined. We derive an analytical expression for
the occupation probability of the walkers at equilibrium in the most general
case. We show that it depends on different order derivatives of the local
reaction functions and not only on the degree of a node, but also on the
average degree of its neighbours at various distances. For such a reason,
reactive random walkers are very sensitive to the structure of a network and
are a powerful way to detect network properties such as symmetries or
degree-degree correlations. As possible applications, we first discuss how the
occupation probability of reactive random walkers can be used to define novel
measures of functional centrality for the nodes of a network. We then
illustrate how network components with the same symmetries can be revealed by
tracking the evolution of reactive walkers. Finally, we show that the dynamics
of our model is influenced by the presence of degree-degree correlations, so
that assortative and disassortative networks can be classified by quantitative
indicators based on reactive walkers