论文标题
偏见的随机步行在梯子图的跨越树上
Biased Random Walk on Spanning Trees of the Ladder Graph
论文作者
论文摘要
我们考虑了一个特定的随机图,该图作为无序培养基,用于进行偏见的随机行走。取一个双面的无限水平梯子,并为(垂直)梯级挑选带有一定边缘重量$ c $的随机跨越树。现在,在那棵跨越的树上随机步行,右边有偏见$β> 1 $。与文献中考虑的其他随机图(随机渗透簇,加尔顿 - 瓦特森树)相反,该图允许基于图形分解为独立片段进行明确的分析。 我们为有偏见的随机步行的速度提供明确的公式,这是偏见$β$和边缘权重$ c $的函数。我们得出的结论是,速度是$β$的连续,单峰函数,并且仅当$β<β_c^{(1)} $仅当$β<β_c^{(1)} $,对于显式临界值$β_C^{(1)} $,具体取决于$ c $。特别是,$β_C^{(1)} $的相变是二阶。 我们表明,另一个二阶相变发生在另一个临界值$β_c^{(2)} <β_c^{(1)} $上,这也很明确:以$β<β_c^{(2)} $ $β<β_c^{(2)} $ Time Time Times Walker在陷阱中花费了第二次,并且在限制局限上是限制限制的核心限制的核心位置,这是一个核心限制的核心位置。我们看到$β_C^{(2)} $小于实现速度最大值的$β$的值。最后,关于线性响应,我们通过证明中心极限定理并计算方差来确认无偏模型的爱因斯坦关系($β= 1 $)。
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 $β>1$ 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 $β$ and the edge weight $c$. We conclude that the speed is a continuous, unimodal function of $β$ that is positive if and only if $β< β_c^{(1)}$ for an explicit critical value $β_c^{(1)}$ depending on $c$. In particular, the phase transition at $β_c^{(1)}$ is of second order. We show that another second order phase transition takes place at another critical value $β_c^{(2)}<β_c^{(1)}$ that is also explicitly known: For $β<β_c^{(2)}$ 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 $β_c^{(2)}$ is smaller than the value of $β$ which achieves the maximal value of the speed. Finally, concerning linear response, we confirm the Einstein relation for the unbiased model ($β=1$) by proving a central limit theorem and computing the variance.