Biased Random Walk on Spanning Trees of the Ladder Graph

Abstract

We consider a specific random graph which serves as a disordered medium for a particle performing biased random walk. Take a two-sided infinite horizontal ladder and pick a random spanning tree with a certain edge weight $c$ for the (vertical) rungs. Now take a random walk on that spanning tree with a bias $\beta >1$ to the right. In contrast to other random graphs considered in the literature (random percolation clusters, Galton-Watson trees) this one allows for an explicit analysis based on a decomposition of the graph into independent pieces. We give an explicit formula for the speed of the biased random walk as a function of both the bias $\beta$ and the edge weight $c$. We conclude that the speed is a continuous, unimodal function of $\beta$ that is positive if and only if $\beta <{\beta }_c^{(1)}$ for an explicit critical value ${\beta }_c^{(1)}$ depending on $c$. In particular, the phase transition at ${\beta }_c^{(1)}$ is of second order. We show that another second order phase transition takes place at another critical value ${\beta }_c^{(2)}<{\beta }_c^{(1)}$ that is also explicitly known: For $\beta <{\beta }_c^{(2)}$ the times the walker spends in traps have second moments and (after subtracting the linear speed) the position fulfills a central limit theorem. We see that ${\beta }_c^{(2)}$ is smaller than the value of $\beta$ which achieves the maximal value of the speed. Finally, concerning linear response, we confirm the Einstein relation for the unbiased model ($\beta =1$) by proving a central limit theorem and computing the variance.

29 pages, 9 figures

References in corpus (4)

• The asymptotic speed of reaction fronts in active reaction-diffusion systems
• Bias induced drift and trapping on random combs and the Bethe lattice: Fluctuation regime and first order phase transitions
• The tail of the length of an excursion in a trap of random size
• Einstein relation for random walk in a one-dimensional percolation model