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：理解和刻画城市基础设施的脆弱性具有重要价值。城市基础设施是维持城市正常运行所必需的工程设施，通常以网络形式存在。潜在应用包括保护脆弱设施、设计鲁棒拓扑结构等。由于不同拓扑特征与基础设施脆弱性之间存在强相关性，且其演化机制复杂，一些启发式方法和机器辅助分析难以有效应对此类场景。本文将相互依赖网络建模为异质图，并提出一种基于图神经网络与强化学习的系统，该系统可在真实数据上训练，以精确刻画城市系统的脆弱性。所提出的系统利用深度学习技术理解并分析异质图，从而能够捕捉级联失效风险并发现城市中的脆弱基础设施。通过多种请求下的大量实验，不仅验证了系统的表达能力，还证明了其迁移能力以及特定组件的必要性。