# A Graph Transformer-Driven Approach for Network Robustness Learning

arXiv: https://arxiv.org/abs/2306.06913 · HTML: https://mlsanity.com/paper/2306.06913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 59 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [A Generalization of Transformer Networks to Graphs](https://mlsanity.com/paper/2012.09699/) (2020)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Representing Long-Range Context for Graph Neural Networks with Global Attention](https://mlsanity.com/paper/2201.08821/) (2022)
- [Universal Graph Transformer Self-Attention Networks](https://mlsanity.com/paper/1909.11855/) (2019)
- [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://mlsanity.com/paper/1909.00136/) (2019)
- [Predicting Network Controllability Robustness: A Convolutional Neural Network Approach](https://mlsanity.com/paper/1908.09471/) (2019)
- [Toward Stronger Robustness of Network Controllability: A Snapback Network Model](https://mlsanity.com/paper/1801.06082/) (2018)
- [A Learning Convolutional Neural Network Approach for Network Robustness Prediction](https://mlsanity.com/paper/2203.10552/) (2022)
- [Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition](https://mlsanity.com/paper/1912.12416/) (2019)
- [Smart Rewiring for Network Robustness](https://mlsanity.com/paper/1303.5269/) (2013)
- [Controllability of networked MIMO systems](https://mlsanity.com/paper/1505.01255/) (2015)
- [Networkcontrology](https://mlsanity.com/paper/1510.08320/) (2015)

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

- [HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness](https://mlsanity.com/paper/2512.22014/) (2025)

## Abstract (arXiv)

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks. Traditionally, network robustness is determined by attack simulations, which is very time-consuming and even incapable for large-scale networks. Network Robustness Learning, which is dedicated to learning network robustness with high precision and high speed, provides a powerful tool to analyze network robustness by replacing simulations. In this paper, a novel versatile and unified robustness learning approach via graph transformer (NRL-GT) is proposed, which accomplishes the task of controllability robustness learning and connectivity robustness learning from multiple aspects including robustness curve learning, overall robustness learning, and synthetic network classification. Numerous experiments show that: 1) NRL-GT is a unified learning framework for controllability robustness and connectivity robustness, demonstrating a strong generalization ability to ensure high precision when training and test sets are distributed differently; 2) Compared to the cutting-edge methods, NRL-GT can simultaneously perform network robustness learning from multiple aspects and obtains superior results in less time. NRL-GT is also able to deal with complex networks of different size with low learning error and high efficiency; 3) It is worth mentioning that the backbone of NRL-GT can serve as a transferable feature learning module for complex networks of different size and different downstream tasks.