Code for Graph-Revised Convolutional Network (ECML-PKDD 2020)
python >= 3.6.0
pytorch = 1.5.0
tqdm
itermplot
The code is based on pyg. Please see instructions for its installation.
dataprocess.py is used for data spliting, edge sampling, and data loader.
./run_fixed.sh 1(GPU No.) GRCN Cora(dataset: Cora, CiteSeer, PubMed) --sparse
To save the log result, add --save in the command.
You can change the parameters of run_fixed.sh and config/.
./run_random.sh 1(GPU No.) GRCN Cora(dataset: Cora, CiteSeer, PubMed, CoraFull, Computers, CS) --sparse
When running on PubMed dataset, add --keep_train_num.
To save the log result, add --save in the command.
You can change the parameters of run_random.sh and config/.
Our model achieves the following performance on :
semi-supervised node classification (fixed split)
| Model | Cora | CiteSeer | PubMed |
|---|---|---|---|
| GCN | 81.4±0.5 | 70.9±0.5 | 79.0±0.3 |
| GAT | 83.2±0.7 | 72.6±0.6 | 78.8±0.3 |
| LDS | 84.0±0.4 | 74.8±0.5 | N/A |
| GLCN | 81.8±0.6 | 70.8±0.5 | 78.8±0.4 |
| Fast-GRCN | 83.6±0.4 | 72.9±0.6 | 79.0±0.2 |
| GRCN | 84.2±0.4 | 73.6±0.5 | 79.0±0.2 |
| Model | Cora | CiteSeer | PubMed |
|---|---|---|---|
| GCN | 81.2±1.9 | 69.8±1.9 | 77.7±2.9 |
| GAT | 81.7±1.9 | 68.8±1.8 | 77.7±3.2 |
| LDS | 81.6±1.0 | 71.0±0.9 | N/A |
| GLCN | 81.4±1.9 | 69.8±1.8 | 77.2±3.2 |
| Fast-GRCN | 83.8±1.6 | 72.3±1.4 | 77.6±3.2 |
| GRCN | 83.7±1.7 | 72.6±1.3 | 77.9±0.2 |