This DGL example implements the model proposed in the paper Deep Graph Contrastive Representation Learning.
Author's code: https://github.com/CRIPAC-DIG/GRACE
This example was implemented by Hengrui Zhang when he was an applied scientist intern at AWS Shanghai AI Lab.
- Python 3.7
- PyTorch 1.7.1
- dgl 0.6.0
'Cora', 'Citeseer' and 'Pubmed'
| Dataset | # Nodes | # Edges | # Classes |
|---|---|---|---|
| Cora | 2,708 | 10,556 | 7 |
| Citeseer | 3,327 | 9,228 | 6 |
| Pubmed | 19,717 | 88,651 | 3 |
--dataname str The graph dataset name. Default is 'cora'.
--gpu int GPU index. Default is 0.
--split int Dataset spliting method. Default is 'random'.
In the paper(as well as authors' repo), the training set and testing set are split randomly with 1:9 ratio. In order to fairly compare it with other models with the split they used, termed public split, in this repo we also provide experiment results using public split. To run the examples,
# Cora with random split
python main.py --dataname cora
# Cora with public split
python main.py --dataname cora --split publicreplace 'cora' with 'citeseer' or 'pubmed' if you would like to run this example for other datasets.
We use the same hyperparameter settings as provided by the author, you can check config.yaml for detailed hyper-parameters for each dataset.
Random split (Train/Test = 1:9)
| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| Accuracy Reported | 83.3 | 72.1 | 86.7 |
| Author's Code | 83.1 | 71.0 | 86.3 |
| DGL | 83.4 | 71.4 | 86.1 |
Public split
| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| Author's Code | 79.9 | 68.6 | 81.3 |
| DGL | 80.1 | 68.9 | 81.2 |