# Graph Vulnerability and Robustness: A Survey

arXiv: https://arxiv.org/abs/2105.00419 · HTML: https://mlsanity.com/paper/2105.00419/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2021.

## Built on by (most cited first, 14 of 14)

- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [Code Recommendation for Open Source Software Developers](https://mlsanity.com/paper/2210.08332/) (2022)
- [Exploring Robust Architectures for Deep Artificial Neural Networks](https://mlsanity.com/paper/2106.15850/) (2021)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [Backdoor Attacks in Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2301.09732/) (2023)
- [A Fast Algorithm for Moderating Critical Nodes via Edge Removal](https://mlsanity.com/paper/2309.06392/) (2023)
- [Network Report: A Structured Description for Network Datasets](https://mlsanity.com/paper/2206.03635/) (2022)
- [SPP-CNN: An Efficient Framework for Network Robustness Prediction](https://mlsanity.com/paper/2305.07872/) (2023)
- [Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling](https://mlsanity.com/paper/2308.08012/) (2023)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting](https://mlsanity.com/paper/2409.05573/) (2024)
- [Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids](https://mlsanity.com/paper/2503.00786/) (2025)
- [Learning to Compress Graphs via Dual Agents for Consistent Topological Robustness Evaluation](https://mlsanity.com/paper/2511.18958/) (2025)
- [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)

The study of network robustness is a critical tool in the characterization and sense making of complex interconnected systems such as infrastructure, communication and social networks. While significant research has been conducted in all of these areas, gaps in the surveying literature still exist. Answers to key questions are currently scattered across multiple scientific fields and numerous papers. In this survey, we distill key findings across numerous domains and provide researchers crucial access to important information by--(1) summarizing and comparing recent and classical graph robustness measures; (2) exploring which robustness measures are most applicable to different categories of networks (e.g., social, infrastructure; (3) reviewing common network attack strategies, and summarizing which attacks are most effective across different network topologies; and (4) extensive discussion on selecting defense techniques to mitigate attacks across a variety of networks. This survey guides researchers and practitioners in navigating the expansive field of network robustness, while summarizing answers to key questions. We conclude by highlighting current research directions and open problems.