Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us. Potential applications include protecting fragile facilities and designing robust topologies, etc. Due to the strong correlation between different topological characteristics and infrastructure vulnerability and their complicated evolution mechanisms, some heuristic and machine-assisted analysis fall short in addressing such a scenario. In this paper, we model the interdependent network as a heterogeneous graph and propose a system based on graph neural network with reinforcement learning, which can be trained on real-world data, to characterize the vulnerability of the city system accurately. The presented system leverages deep learning techniques to understand and analyze the heterogeneous graph, which enables us to capture the risk of cascade failure and discover vulnerable infrastructures of cities. Extensive experiments with various requests demonstrate not only the expressive power of our system but also transferring ability and necessity of the specific components.

翻译：理解和表征城市基础设施的脆弱性具有重要价值，这些基础设施是指维持城市正常运行所必需的工程设施，并以网络形式自然存在。潜在应用包括保护脆弱设施、设计鲁棒拓扑等。由于不同拓扑特征与基础设施脆弱性之间存在强相关性及复杂的演化机制，一些启发式及机器辅助分析方法难以应对此类场景。本文将相依网络建模为异构图，提出了一种基于图神经网络与强化学习的系统，该系统可在真实世界数据上训练，从而精确表征城市系统的脆弱性。所提出的系统利用深度学习技术理解与分析异构图，使我们能够捕获级联故障风险并发现城市的脆弱基础设施。针对各种请求开展的广泛实验不仅证明了该系统的表达能力，还验证了其迁移能力及特定组件的必要性。