This repository contains the paper list of Graph Out-of-Distribution (OOD) 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.
We will try our best to make this paper list updated. If you notice some related papers missing, do not hesitate to contact us via pull requests at our repo.
Papers
Data
Graph Data Augmentation
[ICML 2022] G-Mixup: Graph Data Augmentation for Graph Classification [paper]
[KDD 2022] Graph Rationalization with Environment-based Augmentations [paper]
[NeurIPS 2021] Metropolis-Hastings Data Augmentation for Graph Neural Networks [paper]
[AAAI 2021] Data Augmentation for Graph Neural Networks [paper]
[CVPR 2022] Robust Optimization as Data Augmentation for Large-scale Graphs [paper]
[NeurIPS 2020] Graph Random Neural Network for Semi-Supervised Learning on Graphs [paper]
[ICLR 2020] DropEdge: Towards Deep Graph Convolutional Networks on Node Classification [paper]
Model
Disentanglement-based Graph Models
[TKDE 2022] Disentangled Graph Contrastive Learning With Independence Promotion [paper]
[NeurIPS 2022] GOOD: A Graph Out-of-Distribution Benchmark [paper]
[NeurIPS 2021 Workshop] A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs [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]
Cite
Please consider citing our survey paper if you find this repository helpful:
代码语言:javascript
复制
@article{li2022ood,
title={Out-of-distribution generalization on graphs: A survey},
author={Li, Haoyang and Wang, Xin and Zhang, Ziwei and Zhu, Wenwu},
journal={arXiv preprint arXiv:2202.07987},
year={2022}
}