提出$I^3$模型，精准预测城市多基础设施联动崩溃。
Predicting Cascade Failures in Interdependent Urban Infrastructure Networks
- 用双图自编码+异质图建模基础设施内与间故障传播。
- 预测准确率提升超20%，崩溃规模预测误差降低28.52%。
- 适合城市安全、应急管理等需要跨系统风险评估的场景。
级联失效（CF）指基础设施组件故障在系统中扩散，引发整体崩溃。预测CF对保障基础设施稳定和城市功能至关重要。尽管已有大量研究关注电力、道路等单一网络的CF，但不同基础设施间的相互依赖关系仍被忽视，且复杂演化下捕捉单个网络内部的失效动态面临挑战。为此，本文提出集成式互依基础设施级联失效模型（$I^3$），可同时建模单个网络内部及跨网络的失效动态。$I^3$采用双图自编码器结合全局池化处理网络内动态，并使用异质图建模网络间交互；引入初始节点增强预训练策略缓解图卷积导致的过平滑问题。实验表明，$I^3$在预测基础设施故障上较领先模型提升31.94% AUC、18.03% Precision、29.17% Recall、22.73% F1-score；在级联规模预测上RMSE降低28.52%。模型能准确识别互联与孤立网络中的相变点，纠正仅针对单一网络设计模型的偏差。代码已开源：https://github.com/tsinghua-fib-lab/Icube。
原文摘要 · Abstract (English)
Cascading failures (CF) entail component breakdowns spreading through infrastructure networks, causing system-wide collapse. Predicting CFs is of great importance for infrastructure stability and urban function. Despite extensive research on CFs in single networks such as electricity and road networks, interdependencies among diverse infrastructures remain overlooked, and capturing intra-infrastructure CF dynamics amid complex evolutions poses challenges. To address these gaps, we introduce the \textbf{I}ntegrated \textbf{I}nterdependent \textbf{I}nfrastructure CF model ($I^3$), designed to capture CF dynamics both within and across infrastructures. $I^3$ employs a dual GAE with global pooling for intra-infrastructure dynamics and a heterogeneous graph for inter-infrastructure interactions. An initial node enhancement pre-training strategy mitigates GCN-induced over-smoothing. Experiments demonstrate $I^3$ achieves a 31.94\% in terms of AUC, 18.03\% in terms of Precision, 29.17\% in terms of Recall, 22.73\% in terms of F1-score boost in predicting infrastructure failures, and a 28.52\% reduction in terms of RMSE for cascade volume forecasts compared to leading models. It accurately pinpoints phase transitions in interconnected and singular networks, rectifying biases in models tailored for singular networks. Access the code at https://github.com/tsinghua-fib-lab/Icube.
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