This DGL example implements the GNN model proposed in the paper Towards Deeper Graph Neural Networks.
Paper link: https://arxiv.org/abs/2007.09296
Author's code: https://github.com/divelab/DeeperGNN
Contributor: Liu Tang (@lt610)
- Python 3.6.10
- PyTorch 1.4.0
- numpy 1.18.1
- dgl 0.5.3
- tqdm 4.44.1
The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
|---|---|---|---|---|---|---|---|
| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
--dataset str The graph dataset name. Default is 'Cora'.
--gpu int GPU index. Default is -1, using CPU.
--runs int Number of training runs. Default is 1
--epochs int Number of training epochs. Default is 1500.
--early-stopping int Early stopping patience rounds. Default is 100.
--lr float Adam optimizer learning rate. Default is 0.01.
--lamb float L2 regularization coefficient. Default is 5e-3.
--k int Number of propagation layers. Default is 10.
--hid-dim int Hidden layer dimensionalities. Default is 64.
--dropout float Dropout rate Default is 0.8
Train a model which follows the original hyperparameters on different datasets.
# Cora:
python main.py --dataset Cora --gpu 0 --runs 100 --lamb 0.005 --k 12
# Citeseer:
python main.py --dataset Citeseer --gpu 0 --runs 100 --lamb 0.02 --k 16
# Pubmed:
python main.py --dataset Pubmed --gpu 0 --runs 100 --lamb 0.005 --k 20| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| Accuracy Reported(100 runs) | 84.4 ± 0.5 | 73.3 ± 0.6 | 80.5 ± 0.5 |
| Accuracy DGL(100 runs) | 84.3 ± 0.5 | 73.1 ± 0.9 | 80.5 ± 0.4 |