% Key papers for Graph out-of-distribution generalization (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2023_drugood_3506,
  title = {{DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25970},
}

@inproceedings{neurips2022_learning_64181,
  title = {{Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54643},
}

@inproceedings{iclr2022_handling_31067,
  title = {{Handling Distribution Shifts on Graphs: An Invariance Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7180},
}

@inproceedings{neurips2022_good_64121,
  title = {{GOOD: A Graph Out-of-Distribution Benchmark}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55695},
}

@inproceedings{aaai2024_goodat_6093,
  title = {{GOODAT: Towards Test-Time Graph Out-of-Distribution Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29480},
}

@inproceedings{aaai2024_graph_6155,
  title = {{Graph Invariant Learning with Subgraph Co-mixup for Out-of-Distribution Generalization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28700},
}

@inproceedings{neurips2022_learning_65389,
  title = {{Learning Invariant Graph Representations for Out-of-Distribution Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55398},
}

@inproceedings{cvpr2023_mind_21566,
  title = {{Mind the Label Shift of Augmentation-Based Graph OOD Generalization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23236},
}

@inproceedings{neurips2023_joint_70057,
  title = {{Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69915},
}

@inproceedings{icml2021_size_39954,
  title = {{Size-Invariant Graph Representations for Graph Classification Extrapolations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9239},
}

@inproceedings{iclr2023_energy_33444,
  title = {{Energy-based Out-of-Distribution Detection for Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11526},
}

@inproceedings{neurips2022_graphde_65894,
  title = {{GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54922},
}

@inproceedings{neurips2023_unleashing_69083,
  title = {{Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70792},
}

@inproceedings{cvpr2024_improving_23775,
  title = {{Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30950},
}

@inproceedings{aaai2025_structural_10856,
  title = {{Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33897},
}

@inproceedings{neurips2023_does_67445,
  title = {{Does Invariant Graph Learning via Environment Augmentation Learn Invariance?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72288},
}

@inproceedings{neurips2023_spectral_68124,
  title = {{Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71660},
}

@inproceedings{aaai2025_out_10230,
  title = {{Out-of-Distribution Generalization on Graphs via Progressive Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33414},
}

@inproceedings{aaai2025_subgraph_10867,
  title = {{Subgraph Aggregation for Out-of-Distribution Generalization on Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34065},
}

@inproceedings{neurips2024_revisiting_73531,
  title = {{Revisiting Score Propagation in Graph Out-of-Distribution Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93946},
}

@inproceedings{aaai2025_expand_9046,
  title = {{Expand Horizon: Graph Out-of-Distribution Generalization via Multi-Level Environment Inference}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33444},
}

@inproceedings{aaai2024_maxent_6610,
  title = {{MaxEnt Loss: Calibrating Graph Neural Networks under Out-of-Distribution Shift (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30487},
}

@inproceedings{icml2025_graph_46871,
  title = {{Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45707},
}

@inproceedings{neurips2024_dissecting_70690,
  title = {{Dissecting the Failure of Invariant Learning on Graphs}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96433},
}

@inproceedings{neurips2024_bridging_74068,
  title = {{Bridging OOD Detection and Generalization: A Graph-Theoretic View}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93462},
}

@inproceedings{aaai2026_llm_107259,
  title = {{LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39611},
}

@inproceedings{icml2024_empowering_45611,
  title = {{Empowering Graph Invariance Learning with Deep Spurious Infomax}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32841},
}

@inproceedings{icml2025_topology_46057,
  title = {{Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46500},
}

@inproceedings{icml2026_what_55377,
  title = {{What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=ySUfgY6twT},
}

@inproceedings{neurips2025_quantifying_76401,
  title = {{Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=IpYEKM0Bla},
}

@inproceedings{neurips2025_geometric_78605,
  title = {{Geometric Logit Decoupling for Energy-Based Graph Out-of-distribution Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=gPPPfwU4jj},
}

@inproceedings{neurips2025_refining_79036,
  title = {{Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=kxuIIMBVTg},
}

@inproceedings{neurips2025_pruning_79125,
  title = {{Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=lvDsc5lxma},
}