% Key papers for Graph neural network robustness (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2019_adversarial_28847,
  title = {{Adversarial Attacks on Graph Neural Networks via Meta Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/826},
}

@inproceedings{icml2019_adversarial_37140,
  title = {{Adversarial Attacks on Node Embeddings via Graph Poisoning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4072},
}

@inproceedings{icml2018_adversarial_36500,
  title = {{Adversarial Attack on Graph Structured Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2294},
}

@inproceedings{aaai2022_robust_2657,
  title = {{Robust Heterogeneous Graph Neural Networks against Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04363-robust-heterogeneous-graph-neural-networks-against-adversarial-attacks/},
}

@inproceedings{neurips2019_certifiable_57986,
  title = {{Certifiable Robustness to Graph Perturbations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13898},
}

@inproceedings{aaai2021_uag_1569,
  title = {{UAG: Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07404-uag-uncertainty-aware-attention-graph-neural-network-for-defending-adversarial-attacks/},
}

@inproceedings{cvpr2023_adversarially_20367,
  title = {{Adversarially Robust Neural Architecture Search for Graph Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22055},
}

@inproceedings{cvpr2023_rate_21982,
  title = {{Rate Gradient Approximation Attack Threats Deep Spiking Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22925},
}

@inproceedings{neurips2021_not_62656,
  title = {{Not All Low-Pass Filters are Robust in Graph Convolutional Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28435},
}

@inproceedings{cvpr2023_turning_22426,
  title = {{Turning Strengths Into Weaknesses: A Certified Robustness Inspired Attack Framework Against Graph Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21859},
}

@inproceedings{neurips2020_certified_59524,
  title = {{Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18384},
}

@inproceedings{neurips2020_adversarial_59334,
  title = {{Adversarial Attacks on Deep Graph Matching}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/19008},
}

@inproceedings{aaai2024_enhancing_5866,
  title = {{Enhancing the Robustness of Spiking Neural Networks with Stochastic Gating Mechanisms}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27804},
}

@inproceedings{cvpr2022_appearance_18295,
  title = {{Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2022_are_66446,
  title = {{Are Defenses for Graph Neural Networks Robust?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53115},
}

@inproceedings{aaai2023_task_4779,
  title = {{Task and Model Agnostic Adversarial Attack on Graph Neural Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26761},
}

@inproceedings{aaai2023_local_4085,
  title = {{Local-Global Defense against Unsupervised Adversarial Attacks on Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25979},
}

@inproceedings{aaai2024_spear_7392,
  title = {{Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29239},
}

@inproceedings{neurips2021_graph_62083,
  title = {{Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://openreview.net/forum?id=NxWUnvwFV4},
}

@inproceedings{aaai2023_revisiting_4530,
  title = {{Revisiting Item Promotion in GNN-Based Collaborative Filtering: A Masked Targeted Topological Attack Perspective}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26774},
}

@inproceedings{aaai2024_coupling_5520,
  title = {{Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29203},
}

@inproceedings{aaai2025_refine_10495,
  title = {{Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33471},
}

@inproceedings{aaai2024_value_7756,
  title = {{Value at Adversarial Risk: A Graph Defense Strategy against Cost-Aware Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29282},
}

@inproceedings{aaai2025_hypergraph_9478,
  title = {{Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32005},
}

@inproceedings{cvpr2025_towards_27898,
  title = {{Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/32720},
}

@inproceedings{neurips2024_robust_73127,
  title = {{Robust Graph Neural Networks via Unbiased Aggregation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94303},
}

@inproceedings{neurips2024_enhancing_74656,
  title = {{Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92939},
}

@inproceedings{aaai2025_grimm_9373,
  title = {{Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending Against Poisoning Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34064},
}

@inproceedings{icml2024_robust_45235,
  title = {{Robust Stable Spiking Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33217},
}

@inproceedings{iclr2024_adversarial_35320,
  title = {{Adversarial Attacks on Fairness of Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17753},
}

@inproceedings{iclr2025_robustness_83801,
  title = {{Robustness Inspired Graph Backdoor Defense}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28019},
}

@inproceedings{aaai2026_edge_106778,
  title = {{Edge Self-Adversarial Augmentation Enhances Graph Contrastive Learning Against Neighborhood Inconsistency}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39085},
}

@inproceedings{aaai2025_alleviating_8091,
  title = {{Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33244},
}

@inproceedings{icml2025_self_49084,
  title = {{Self-supervised Adversarial Purification for Graph Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43540},
}

@inproceedings{neurips2024_if_73861,
  title = {{If You Want to Be Robust, Be Wary of Initialization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93648},
}

@inproceedings{neurips2025_robust_80005,
  title = {{Robust Graph Condensation via Classification Complexity Mitigation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vATe64ktAo},
}

@inproceedings{iclr2025_exact_82796,
  title = {{Exact Certification of (Graph) Neural Networks Against Label Poisoning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29003},
}

@inproceedings{iclr2025_provably_83101,
  title = {{Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28705},
}