Robustness of weighted networks

加权网络 二进制数 计算机科学 节点(物理) 稳健性(进化) 度量(数据仓库) 秩(图论) 复杂网络 数据挖掘 数学 算术 工程类 基因 组合数学 万维网 结构工程 生物化学 化学
作者
Michele Bellingeri,Davide Cassi
出处
期刊:Physica D: Nonlinear Phenomena [Elsevier BV]
卷期号:489: 47-55 被引量:73
标识
DOI:10.1016/j.physa.2017.07.020
摘要

Abstract Complex network response to node loss is a central question in different fields of network science because node failure can cause the fragmentation of the network, thus compromising the system functioning. Previous studies considered binary networks where the intensity (weight) of the links is not accounted for, i.e. a link is either present or absent. However, in real-world networks the weights of connections, and thus their importance for network functioning, can be widely different. Here, we analyzed the response of real-world and model networks to node loss accounting for link intensity and the weighted structure of the network. We used both classic binary node properties and network functioning measure, introduced a weighted rank for node importance (node strength), and used a measure for network functioning that accounts for the weight of the links (weighted efficiency). We find that: (i) the efficiency of the attack strategies changed using binary or weighted network functioning measures, both for real-world or model networks; (ii) in some cases, removing nodes according to weighted rank produced the highest damage when functioning was measured by the weighted efficiency; (iii) adopting weighted measure for the network damage changed the efficacy of the attack strategy with respect the binary analyses. Our results show that if the weighted structure of complex networks is not taken into account, this may produce misleading models to forecast the system response to node failure, i.e. consider binary links may not unveil the real damage induced in the system. Last, once weighted measures are introduced, in order to discover the best attack strategy, it is important to analyze the network response to node loss using nodes rank accounting the intensity of the links to the node.
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