Recent global emergencies emphasize the critical role of reliable communication networks. As dependence on critical infrastructures grows, the focus shifts from isolated failures to designing networks capable of withstanding disasters, taking into account their interdependence with infrastructures like the power grid. This paper investigates the problem of the disaster resilient upgrade of interdependent networks, focusing on enhancing network resilience during emergencies and ensuring a service-level agreement. We analyze how the interdependency between the networks affects the disaster resilience and propose heuristic methods for network operators to improve resilience against disasters. Furthermore, to address the challenge of hidden interdependencies, we present a novel approach using graph neural networks for predicting interdependency between networks based on historical data of failures. Using simulations with real networks and earthquake data, we demonstrate that limiting the number of interdependent edges per node significantly affects resilience. We show that if sufficient data is available graph neural networks can learn the connection between failures and interdependencies, and capable of predicting interdependencies. Additionally, we show that selecting appropriate upgrade methods can reduce network upgrade costs by up to 20%.

中文翻译：

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使用机器学习对具有灾害弹性的相互依赖网络进行建模和升级


最近的全球紧急情况凸显了可靠通信网络的关键作用。随着对关键基础设施的依赖程度增加，重点从孤立的故障转移到设计能够承受灾难的网络，同时考虑到它们与电网等基础设施的相互依赖性。本文研究了相互依赖网络的抗灾升级问题，重点关注在紧急情况下增强网络弹性和确保服务水平协议。我们分析了网络之间的相互依赖关系如何影响灾害弹性，并为网络运营商提出了提高灾害韧性的启发式方法。此外，为了解决隐藏的相互依赖关系的挑战，我们提出了一种使用图神经网络的新方法，用于根据故障的历史数据预测网络之间的相互依赖关系。通过使用真实网络和地震数据的模拟，我们证明限制每个节点相互依赖的边缘的数量会显着影响弹性。我们表明，如果有足够的数据可用，图神经网络可以学习故障和相互依赖关系之间的联系，并能够预测相互依赖关系。此外，我们还表明，选择合适的升级方法可以将网络升级成本降低多达 20%。