Graph Vulnerability and Robustness: A Survey
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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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2021.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 1; 2025: 4.
14 papers.
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- 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
- 2022Code Recommendation for Open Source Software Developerscited by 13
- 2021Exploring Robust Architectures for Deep Artificial Neural Networkscited by 11
- 2022GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injectionscited by 10
- 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
- 2023A Fast Algorithm for Moderating Critical Nodes via Edge Removalcited by 3
- 2022Network Report: A Structured Description for Network Datasetscited by 1
- 2023SPP-CNN: An Efficient Framework for Network Robustness Predictioncited by 1
- 2023Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Poolingcited by 1
- 2025Promoting Fairness in Information Access within Social Networkscited by 1
- 2024Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrastingcited by 0
- 2025Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgridscited by 0
- 2025HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness26 Dec 2025
- 2025Promoting Fairness in Information Access within Social Networks8 Dec 2025
- 2025Learning to Compress Graphs via Dual Agents for Consistent Topological Robustness Evaluation24 Nov 2025
- 2025Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids2 Mar 2025
- 2024Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting9 Sep 2024
- 2023A Fast Algorithm for Moderating Critical Nodes via Edge Removal9 Sep 2023
- 2023Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling10 Aug 2023
- 2023SPP-CNN: An Efficient Framework for Network Robustness Prediction13 May 2023
- 2023Backdoor Attacks in Peer-to-Peer Federated Learning23 Jan 2023
- 2022GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections23 Oct 2022
- 2022Code Recommendation for Open Source Software Developers15 Oct 2022
- 2022Network Report: A Structured Description for Network Datasets8 Jun 2022
2 more
- 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
- 2022Code Recommendation for Open Source Software Developerscited by 13
- 2021Exploring Robust Architectures for Deep Artificial Neural Networkscited by 11
- 2022GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injectionscited by 10
- 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
- 2023A Fast Algorithm for Moderating Critical Nodes via Edge Removalcited by 3
- 2022Network Report: A Structured Description for Network Datasetscited by 1
- 2023SPP-CNN: An Efficient Framework for Network Robustness Predictioncited by 1
- 2023Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Poolingcited by 1
- 2025Promoting Fairness in Information Access within Social Networkscited by 1
- 2024Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrastingcited by 0
- 2025Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgridscited by 0
12 most cited of 14 All 14 (JSON)
- 2025HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness26 Dec 2025
- 2025Promoting Fairness in Information Access within Social Networks8 Dec 2025
- 2025Learning to Compress Graphs via Dual Agents for Consistent Topological Robustness Evaluation24 Nov 2025
- 2025Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids2 Mar 2025
- 2024Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting9 Sep 2024
- 2023A Fast Algorithm for Moderating Critical Nodes via Edge Removal9 Sep 2023
- 2023Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling10 Aug 2023
- 2023SPP-CNN: An Efficient Framework for Network Robustness Prediction13 May 2023
- 2023Backdoor Attacks in Peer-to-Peer Federated Learning23 Jan 2023
- 2022GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections23 Oct 2022
- 2022Code Recommendation for Open Source Software Developers15 Oct 2022
- 2022Network Report: A Structured Description for Network Datasets8 Jun 2022
12 newest of 14 All 14 (JSON)