Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network

TL;DR

This paper introduces a novel graph neural network with reinforcement learning to accurately identify vulnerable nodes in interdependent urban infrastructure networks, aiding in city resilience planning.

Contribution

It presents a new system modeling urban infrastructure as a heterogeneous graph and applying deep learning to assess vulnerability, surpassing heuristic methods.

Findings

Effective identification of vulnerable nodes in real-world data

High transferability of the proposed model

Demonstrated superiority over traditional heuristic approaches

Abstract

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.