Suppr超能文献

通过边移除策略抑制交通驱动的流行病传播。

Suppressing traffic-driven epidemic spreading by edge-removal strategies.

作者信息

Yang Han-Xin, Wu Zhi-Xi, Wang Bing-Hong

机构信息

Department of Physics, Fuzhou University, Fuzhou 350108, China.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jun;87(6):064801. doi: 10.1103/PhysRevE.87.064801. Epub 2013 Jun 10.

Abstract

The interplay between traffic dynamics and epidemic spreading on complex networks has received increasing attention in recent years. However, the control of traffic-driven epidemic spreading remains to be a challenging problem. In this Brief Report, we propose a method to suppress traffic-driven epidemic outbreak by properly removing some edges in a network. We find that the epidemic threshold can be enhanced by the targeted cutting of links among large-degree nodes or edges with the largest algorithmic betweenness. In contrast, the epidemic threshold will be reduced by the random edge removal. These findings are robust with respect to traffic-flow conditions, network structures, and routing strategies. Moreover, we find that the shutdown of targeted edges can effectively release traffic load passing through large-degree nodes, rendering a relatively low probability of infection to these nodes.

摘要

近年来,复杂网络上交通动力学与疫情传播之间的相互作用受到了越来越多的关注。然而,控制交通驱动的疫情传播仍然是一个具有挑战性的问题。在本简要报告中,我们提出了一种通过适当移除网络中的一些边来抑制交通驱动的疫情爆发的方法。我们发现,通过有针对性地切断大度节点之间的链路或具有最大算法介数的边,可以提高疫情阈值。相比之下,随机移除边会降低疫情阈值。这些发现对于交通流条件、网络结构和路由策略具有鲁棒性。此外,我们发现关闭有针对性的边可以有效地释放通过大度节点的交通负荷,使这些节点的感染概率相对较低。

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