Cayley 图混合时间的敏感性
Sensitivity of mixing times of Cayley graphs
AI总结:
本文证明全变差混合时间在拟等距下不是不变的,通过构造度数无界的 Cayley 图对及有界度非传递图,展示混合时间之比可趋于无穷大。
摘要:
我们证明全变差混合时间不是拟等距不变的,即使对于 Cayley 图也是如此。即,我们构造了一系列 Cayley 图对,它们之间的映射以有界方式扭曲度量,而两个混合时间之比趋于无穷大。作为例子的 Cayley 图具有无界度数。对于非传递图,我们构造了有界度图,其中从一个图的最差起始点出发的混合时间,渐近地小于通过将某些边权重从 1 增加到 $1+o(1)$ 所得网络上随机游走从最佳起始点出发的混合时间。
英文摘要:
We show that the total variation mixing time is not quasi-isometry invariant, even for Cayley graphs. Namely, we construct a sequence of pairs of Cayley graphs with maps between them that twist the metric in a bounded way, while the ratio of the two mixing times goes to infinity. The Cayley graphs serving as an example have unbounded degrees. For non-transitive graphs we construct bounded degree graphs for which the mixing time from the worst starting point for one graph is asymptotically smaller than the mixing time from the best starting point of the random walk on a network obtained by increasing some of the edge weights from 1 to $1+o(1)$.
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