## Out-Of-Distribution Generalization on Graphs: Paper List |

Paper list of **Graph Out-of-Distribution Generalization**. The existing literature can be summarized into three categories from conceptually different perspectives, i.e., *data*, *model*, and *learning strategy*, based on their positions in the graph machine learning pipeline. For more details, please refer to our survey paper **Out-Of-Distribution Generalization on Graphs: A Survey**.

- [CVPR 2023] Mind the Label Shift of Augmentation-based Graph OOD Generalization [paper]
- [arXiv 2022] Adversarial Causal Augmentation for Graph Covariate Shift [paper]
- [NeurIPS 2022] Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks [paper]
- [ICML 2022] G-Mixup: Graph Data Augmentation for Graph Classification [paper]
- [ICML 2022] Local Augmentation for Graph Neural Networks [paper]
- [KDD 2022] Graph Rationalization with Environment-based Augmentations [paper]
- [CVPR 2022] Robust Optimization as Data Augmentation for Large-scale Graphs [paper]
- [NeurIPS 2021] Metropolis-Hastings Data Augmentation for Graph Neural Networks [paper]
- [AAAI 2021] Data Augmentation for Graph Neural Networks [paper]
- [NeurIPS 2020] Graph Random Neural Network for Semi-Supervised Learning on Graphs [paper]

- [TKDE 2022] Disentangled Graph Contrastive Learning With Independence Promotion [paper]
- [NeurIPS 2022] Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure [paper]
- [NeurIPS 2021] Disentangled Contrastive Learning on Graphs [paper]
- [AAAI 2020] Independence Promoted Graph Disentangled Networks [paper]
- [NeurIPS 2020] Factorizable Graph Convolutional Networks [paper]
- [KDD 2020] Interpretable deep graph generation with node-edge co-disentanglement [paper]
- [ICML 2019] Disentangled Graph Convolutional Networks [paper]

- [TKDE 2022] OOD-GNN: Out-of-Distribution Generalized Graph Neural Network [paper]
- [NeurIPS 2022] OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs [paper]
- [NeurIPS 2022] Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs [paper]
- [ICML 2022] Learning from Counterfactual Links for Link Prediction [paper]
- [KDD 2022] Causal Attention for Interpretable and Generalizable Graph Classification [paper]
- [arXiv 2022] Deconfounding to Explanation Evaluation in Graph Neural Networks [paper]
- [TNNLS 2022] Debiased Graph Neural Networks with Agnostic Label Selection Bias [paper]
- [arXiv 2021] Generalizing Graph Neural Networks on Out-Of-Distribution Graphs [paper]
- [ICML 2021] Generative Causal Explanations for Graph Neural Networks [paper]
- [ICML 2021] Size-Invariant Graph Representations for Graph Classification Extrapolations [paper]

- [NeurIPS 2022] Learning Invariant Graph Representations for Out-of-Distribution Generalization [paper]
- [NeurIPS 2022] Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift [paper]
- [NeurIPS 2022] SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks [paper]
- [ICML 2022] Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism [paper]
- [ICLR 2022] Handling Distribution Shifts on Graphs: An Invariance Perspective [paper]
- [ICLR 2022] Discovering Invariant Rationales for Graph Neural Networks [paper]
- [NeurIPS 2021] Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data [paper]
- [arXiv 2021] Stable Prediction on Graphs with Agnostic Distribution Shift [paper]

- [AAAI 2023] Adversarial Weight Perturbation Improves Generalization in Graph Neural Network [paper]
- [arXiv 2021] CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks [paper]
- [arXiv 2021] Distributionally Robust Semi-Supervised Learning Over Graphs [paper]
- [arXiv 2021] Online Adversarial Distillation for Graph Neural Networks [paper]
- [TKDE 2019] Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure [paper]
- [ICDM 2019] Domain-Adversarial Graph Neural Networks for Text Classification [paper]

- [arXiv 2022] Test-Time Training for Graph Neural Networks [paper]
- [arXiv 2022] GraphTTA: Test Time Adaptation on Graph Neural Networks [paper]
- [ICML 2022] Let Invariant Rationale Discovery Inspire Graph Contrastive Learning [paper]
- [WWW 2022] Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift [paper]
- [arXiv 2021] Graph Self-Supervised Learning: A Survey [paper]
- [ICML 2021] From Local Structures to Size Generalization in Graph Neural Networks [paper]
- [NeurIPS 2020] Graph Contrastive Learning with Augmentations [paper]
- [ICLR 2020] Strategies for Pre-training Graph Neural Networks [paper]

- [NeurIPS 2022] Generalization Analysis of Message Passing Neural Networks on Large Random Graphs [paper]
- [NeurIPS 2021] Subgroup Generalization and Fairness of Graph Neural Networks [paper]
- [NeurIPS 2021] Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks [paper]
- [ICLR 2021] How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks [paper]
- [ICLR 2021] A pac-bayesian approach to generalization bounds for graph neural networks [paper]
- [arXiv 2021] Generalization bounds for graph convolutional neural networks via Rademacher complexity [paper]
- [ICML 2021] Graph Convolution for Semi-Supervised Classification Improved Linear Separability and Out-of-Distribution Generalization [paper]
- [ICML 2020 WorkShop] From Graph Low-Rank Global Attention to 2-FWL Approximation [paper]
- [ICML 2020] Generalization and Representational Limits of Graph Neural Networks [paper]
- [NeurIPS 2019] Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels [paper]
- [KDD 2019] Stability and Generalization of Graph Convolutional Neural Networks [paper]
- [Neural Networks] The Vapnikâ€“Chervonenkis dimension of graph and recursive neural networks [paper]

- [arXiv 2022] Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [paper]
- [ICML 2022] Graph Neural Architecture Search Under Distribution Shifts [paper]
- [arXiv 2021] Learning to Pool in Graph Neural Networks for Extrapolation [paper]
- [ICLR 2020] What Can Neural Networks Reason About? [paper]
- [ICLR 2020] Neural Execution of Graph Algorithms [paper]
- [NeurIPS 2019] Understanding Attention and Generalization in Graph Neural Networks [paper]
- [arXiv 2020] Customized Graph Neural Networks [paper]

- [NeurIPS 2022] Association Graph Learning for Multi-Task Classification with Category Shifts [paper]
- [IJCNN 2021] Lifelong Learning of Graph Neural Networks for Open-World Node Classification [paper]

- [NeurIPS 2022] Learning Substructure Invariance for Out-of-Distribution Molecular Representations [paper]
- [arXiv 2022] A critical examination of robustness and generalizability of machine learning [paper]
- [TMLR 2022] How Do Graph Networks Generalize to Large and Diverse Molecular Systems [paper]
- [AAAI 2022] How Does Knowledge Graph Embedding Extrapolate to Unseen Data: a Semantic Evidence View [paper]
- [NeurIPS 2021 Workshop] Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift [paper]
- [ICML 2020 Workshop] Evaluating Logical Generalization in Graph Neural Networks [paper]

- [arXiv 2022] Empowering Graph Representation Learning with Test-Time Graph Transformation [paper]
- [arXiv 2022] Shift-Robust Node Classification via Graph Adversarial Clustering [paper]
- [KDD 2022] Learning on Graphs with Out-of-Distribution Nodes [paper]

- [NeurIPS 2022] GOOD: A Graph Out-of-Distribution Benchmark [paper]
- [arXiv 2022] DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [paper]
- [NeurIPS 2021 Workshop] A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs [paper]

- [arxiv 2022] Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks [paper]
- [NeurIPS 2022] GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs [paper]
- [arXiv 2022] Causally-guided Regularization of Graph Attention Improves Generalizability [paper]