Measuring cascade effects in coupled networks using algebraic connectivity

Sotharith Tauch, William Liu, R. Pears
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Abstract

Understanding how the underlying network structure and interconnectivity impact on robustness of the coupled networks is a major challenge in complex networks studies. There are some existing metrics that can be used to measure network robustness. However, different metrics such as the average node degree, interpret different characteristic of network topological structure, especially less metrics have been identified to effectively measure the cascade performance in coupled networks. In this paper, we propose to use a combined Laplacian matrix to model the coupled networks and their interconnectivity, and then use its algebraic connectivity metric as a measure to its cascading behavior. Moreover, we have conducted extensive comparative studies among different metrics such as the average node degree, and the proposed algebraic connectivity. We have found that the algebraic connectivity metric can describe more accurate and finer characteristics on topological structure of coupled networks than other metrics widely adapted by the existing research studies for measuring the cascading performance in coupled networks.
用代数连通性测量耦合网络中的级联效应
了解潜在的网络结构和互联性如何影响耦合网络的鲁棒性是复杂网络研究中的一个主要挑战。有一些现有的指标可以用来衡量网络的鲁棒性。然而,平均节点度等不同的度量解释了网络拓扑结构的不同特征,特别是在耦合网络中有效衡量级联性能的度量较少。在本文中,我们提出用一个组合拉普拉斯矩阵来建模耦合网络及其连通性,然后用其代数连通性度量作为其级联行为的度量。此外,我们还对不同的度量进行了广泛的比较研究,如平均节点度和所提出的代数连通性。我们发现,与现有研究广泛采用的用于耦合网络级联性能度量的其他度量相比,代数连通性度量能够更准确、更精细地描述耦合网络拓扑结构的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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