PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix]

Yuning You^*, Tianlong Chen^*, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

In NeurIPS 2020.

In this repository, we develop contrastive learning with augmentations for GNN pre-training (GraphCL, Figure 1) to address the challenge of data heterogeneity in graphs. Systematic study is performed as shown in Figure 2, to assess the performance of contrasting different augmentations on various types of datasets.

• The Role of Data Augmentation
• Semi-supervised learning [TU Datasets] [MNIST and CIFAR10]
• Unsupervised representation learning [TU Datasets] [Cora and Citeseer]
• Transfer learning [MoleculeNet and PPI]
• Adversarial robustness [Component Graphs]

Some issues might occur due to the version mismatch. I collect them as follows (keep updating).

• KeyError:'num_nodes' in unsupervised_TU: Shen-Lab#36, Shen-Lab#41
• AttributeError: 'Data' object has no attribute 'cat_dim' in transferLearning_MoleculeNet_PPI: Shen-Lab#13

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

@inproceedings{You2020GraphCL,
 author = {You, Yuning and Chen, Tianlong and Sui, Yongduo and Chen, Ting and Wang, Zhangyang and Shen, Yang},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {5812--5823},
 publisher = {Curran Associates, Inc.},
 title = {Graph Contrastive Learning with Augmentations},
 url = {https://proceedings.neurips.cc/paper/2020/file/3fe230348e9a12c13120749e3f9fa4cd-Paper.pdf},
 volume = {33},
 year = {2020}
}