巨型组件
连接部件
稳健性(进化)
网络拓扑
相互依存的网络
计算机科学
复杂网络
组分(热力学)
渗透(认知心理学)
相互依存
强连通分量
分布式计算
理论(学习稳定性)
网络结构
拓扑(电路)
编队网络
随机图
计算机网络
理论计算机科学
数学
图形
人工智能
物理
算法
法学
生物
生物化学
政治学
化学
组合数学
机器学习
万维网
基因
神经科学
热力学
作者
Maksim Kitsak,Alexander A. Ganin,Daniel A. Eisenberg,Pavel L. Krapivsky,Dmitri Krioukov,David Alderson,Igor Linkov
出处
期刊:Physical review
[American Physical Society]
日期:2018-01-22
卷期号:97 (1): 012309-012309
被引量:62
标识
DOI:10.1103/physreve.97.012309
摘要
We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.
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