Abelian网络中的本质增强:连续性与一致严格单调性
Essential enhancements in Abelian networks: continuity and uniform strict monotonicity
AI总结:
本文研究激活随机游走模型临界曲线的连续性与递增事件概率的严格单调性,并将渗流中的本质增强技术重构于Abelian网络框架。
摘要:
我们证明,在相当广泛的一般性条件下,激活随机游走模型的临界曲线是失活速率的连续函数,并给出了其斜率的一个界,该界关于图的选择是一致的。此外,我们对一大类“递增”事件的概率导出了严格单调性,推广了Rolla和Sidoravicius(2012)的先前结果。我们的证明方法具有独立价值,可视为最初为渗流引入的“本质增强”技术在Abelian网络框架中的重新表述。
英文摘要:
We prove that in wide generality the critical curve of the activated random walk model is a continuous function of the deactivation rate, and we provide a bound on its slope which is uniform with respect to the choice of the graph. Moreover, we derive strict monotonicity properties for the probability of a wide class of `increasing' events,extending previous results of Rolla and Sidoravicius (2012). Our proof method is of independent interest and can be viewed as a reformulation of the `essential enhancements' technique -- which was introduced for percolation -- in the framework of Abelian networks.
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