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arXiv 1104.3789math.PR
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随机正则图上随机游动的病毒过程

Viral processes by random walks on random regular graphs

Mohammed Abdullah, Colin Cooper, Moez Draief

摘要:

研究了带有$k$粒子的SIR传染病模型,该模型在随机正则图上进行独立的随机游动。这里我们假设$k\leq n^ε$,其中$n$是随机图中的顶点数,而$ε$是某个足够小的常数。我们给出了一个边加权图减少的动态过程,使我们能够应用标准的结果Erdens-Rényi随机图的粒子集。特别是,我们展示了模型的参数如何给出两个阈值:在亚临界状态下,$O(\ln k)$粒子被感染。在超临界状态下,对于由模型参数确定的常数$β\in(0,1)$,$βk$以概率$β$感染,而$O(\ln k)$以概率$(1-β)$感染。最后,存在所有$k$颗粒都被感染的方案。此外,边权重给出了关于粒子何时被感染的信息。我们利用这一点,给SI的情况下的过程的完成时间。

英文摘要:

We study the SIR epidemic model with infections carried by $k$ particles making independent random walks on a random regular graph. Here we assume $k\leq n^ε$, where $n$ is the number of vertices in the random graph, and $ε$ is some sufficiently small constant. We give an edge-weighted graph reduction of the dynamics of the process that allows us to apply standard results of Erdős-Rényi random graphs on the particle set. In particular, we show how the parameters of the model give two thresholds: In the subcritical regime, $O(\ln k)$ particles are infected. In the supercritical regime, for a constant $β\in(0,1)$ determined by the parameters of the model, $βk$ get infected with probability $β$, and $O(\ln k)$ get infected with probability $(1-β)$. Finally, there is a regime in which all $k$ particles are infected. Furthermore, the edge weights give information about when a particle becomes infected. We exploit this to give a completion time of the process for the SI case.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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