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利用爆炸渗流进行网络的免疫和靶向破坏。

Immunization and Targeted Destruction of Networks using Explosive Percolation.

作者信息

Clusella Pau, Grassberger Peter, Pérez-Reche Francisco J, Politi Antonio

机构信息

Institute for Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom.

Dipartimento di Fisica, Università di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino, Italy.

出版信息

Phys Rev Lett. 2016 Nov 11;117(20):208301. doi: 10.1103/PhysRevLett.117.208301. Epub 2016 Nov 8.

Abstract

A new method ("explosive immunization") is proposed for immunization and targeted destruction of networks. It combines the explosive percolation (EP) paradigm with the idea of maintaining a fragmented distribution of clusters. The ability of each node to block the spread of an infection (or to prevent the existence of a large cluster of connected nodes) is estimated by a score. The algorithm proceeds by first identifying low score nodes that should not be vaccinated or destroyed, analogously to the links selected in EP if they do not lead to large clusters. As in EP, this is done by selecting the worst node (weakest blocker) from a finite set of randomly chosen "candidates." Tests on several real-world and model networks suggest that the method is more efficient and faster than any existing immunization strategy. Because of the latter property it can deal with very large networks.

摘要

一种用于网络免疫和靶向破坏的新方法(“爆炸式免疫”)被提出来了。它将爆炸渗流(EP)范式与保持簇的碎片化分布的想法相结合。通过一个分数来估计每个节点阻止感染传播(或防止存在大量相连节点的簇)的能力。该算法首先识别不应接种疫苗或被破坏的低分节点,这类似于在EP中如果链接不会通向大簇就选择它们的方式。与EP一样,这是通过从一组有限的随机选择的“候选节点”中选择最差的节点(最弱的阻止者)来完成的。对几个真实世界和模型网络的测试表明,该方法比任何现有的免疫策略更有效、更快。由于后一个特性,它可以处理非常大的网络。

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