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.

对城市基础设施的脆弱性进行建模与分析，利用基于图神经网络和强化学习的系统来准确刻画城市系统的脆弱性，包括城市设施的风险和脆弱性的发现。