论文标题
关于在网络上使用马尔可夫链进行流行模型
On the use of Markov chains for epidemic modeling on networks
论文作者
论文摘要
我们讨论了依赖马尔可夫连锁店的网络流行病的各种模型。在图上随机步行通常用于预测流行病的传播，并研究可能的控制动作以减轻它们。在这项研究中，我们证明它们没有完全反映流行病的动态，因为它们高估了感染时间。因此，我们解释了如何仍可以使用马尔可夫连锁链准确地对病毒扩散进行建模，并正确预测感染时间。我们还提供了一种算法，该算法可以通过采样策略有效地估算感染时间。最后，我们根据感染时间提出了一个新颖的指标，并将其节点排名属性与基于随机步行的其他中心度度量进行了比较。
We discuss various models for epidemics on networks that rely on Markov chains. Random walks on graphs are often used to predict epidemic spread and to investigate possible control actions to mitigate them. In this study, we demonstrate that they do not fully reflect the dynamics of epidemics, as they overestimate infection times. Accordingly, we explain how Markov chains may still be used to accurately model the virus spread, and to correctly predict infection times. We also provide an algorithm that efficiently estimates infection times via a sampling strategy. Finally, we present a novel indicator based on infection times, and we compare its node ranking properties with other centrality measures based on random walks.
