# Predicting Cascade Failures in Interdependent Urban Infrastructure Networks

arXiv: https://arxiv.org/abs/2503.02890 · HTML: https://mlsanity.com/paper/2503.02890/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [GMNN: Graph Markov Neural Networks](https://mlsanity.com/paper/1905.06214/) (2019)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey](https://mlsanity.com/paper/2206.06089/) (2022)
- [Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network](https://mlsanity.com/paper/2307.09866/) (2023)
- [Mitigating Cascading Failures in Interdependent Power Grids and Communication Networks](https://mlsanity.com/paper/1405.2866/) (2014)

## Built on by (most cited first, 1 of 1)

- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)

## Abstract (arXiv)

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 Integrated Interdependent Infrastructure CF model (I3), designed to capture CF dynamics both within and across infrastructures. I3 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 I3 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.