Vertex reinforced random walks with exponential interaction on complete graphs
Benito Pires, Fernando P. A. Prado, and Rafael A. Rosales

TL;DR
This paper models interacting vertex reinforced random walks on complete graphs with exponential dependence on visit proportions, analyzing convergence to equilibria and the effects of interaction strength.
Contribution
It introduces a new model of interacting reinforced random walks with exponential interaction, analyzing convergence and equilibrium properties using Lyapunov functions.
Findings
Empirical vertex occupation measures converge almost surely to the flow's limit set.
Convergence to a unique equilibrium occurs when interaction strengths are below a certain bound.
Examples of repelling random walks illustrate diverse interaction behaviors.
Abstract
We describe a model for vertex reinforced interacting random walks on complete graphs with vertices. The transition probability of a random walk to a given vertex depends exponentially on the proportion of visits made by all walks to that vertex. The individual proportion of visits is modulated by a strength parameter that can be set equal to any real number. This model covers a large variety of interactions including different vertex repulsion and attraction strengths between any two random walks as well as self-reinforced interactions. We show that the process of empirical vertex occupation measures defined by the interacting random walks converges (a.s.) to the limit set of the flow induced by a smooth vector field. Further, if the set of equilibria of the field is formed by isolated points, then the vertex occupation measures converge (a.s.) to an equilibrium of the…
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