Graph Contrastive Learning Automated

PyTorch implementation for Graph Contrastive Learning Automated
[talk]
[poster]
[appendix]

Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

In ICML 2021.

PyTorch implementation for Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

In WSDM 2022.

Overview

In this repository, we propose a principled framework named joint augmentation selection (JOAO), to automatically, adaptively and dynamically select augmentations during GraphCL training.
Sanity check shows that the selection aligns with previous "best practices", as shown in Figure 2.

Dependencies

• torch-geometric >= 1.6.0
• ogb == 1.2.4

Experiments

• Semi-supervised learning [JOAO: TU Datasets] [JOAO: OGB] [LP: TU Datasets] [LP: OGB]
• Unsupervised representation learning [JOAO: TU Datasets]
• Transfer learning [JOAO: MoleculeNet and PPI] [LP: MoleculeNet and PPI]

Citation

If you use this code for you research, please cite our paper.

@article{you2021graph,
  title={Graph Contrastive Learning Automated},
  author={You, Yuning and Chen, Tianlong and Shen, Yang and Wang, Zhangyang},
  journal={arXiv preprint arXiv:2106.07594},
  year={2021}
}