wehub-resource-sync
78 lines
3.2 KiB
Markdown
78 lines
3.2 KiB
Markdown
# Graph Random Neural Network(GRAND)
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This DGL example implements the GNN model proposed in the paper [Graph Random Neural Network for Semi-Supervised Learning on Graphs]( https://arxiv.org/abs/2005.11079).
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Author's code: https://github.com/THUDM/GRAND
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## Example Implementor
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This example was implemented by [Hengrui Zhang](https://github.com/hengruizhang98) when he was an applied scientist intern at AWS Shanghai AI Lab.
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## Dependencies
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- Python 3.7
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- PyTorch 1.7.1
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- dgl 0.5.3
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## Dataset
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The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:
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| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
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| :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
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| Citeseer | 3,327 | 9,228 | 3,703 | 6 | 120 | 500 | 1000 |
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| Cora | 2,708 | 10,556 | 1,433 | 7 | 140 | 500 | 1000 |
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| Pubmed | 19,717 | 88,651 | 500 | 3 | 60 | 500 | 1000 |
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## Arguments
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###### Dataset options
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```
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--dataname str The graph dataset name. Default is 'cora'.
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```
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###### GPU options
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```
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--gpu int GPU index. Default is -1, using CPU.
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```
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###### Model options
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```
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--epochs int Number of training epochs. Default is 2000.
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--early_stopping int Early stopping patience rounds. Default is 200.
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--lr float Adam optimizer learning rate. Default is 0.01.
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--weight_decay float L2 regularization coefficient. Default is 5e-4.
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--dropnode_rate float Dropnode rate (1 - keep probability). Default is 0.5.
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--input_droprate float Dropout rate of input layer. Default is 0.5.
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--hidden_droprate float Dropout rate of hidden layer. Default is 0.5.
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--hid_dim int Hidden layer dimensionalities. Default is 32.
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--order int Propagation step. Default is 8.
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--sample int Sampling times of dropnode. Default is 4.
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--tem float Sharpening temperaturer. Default is 0.5.
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--lam float Coefficient of Consistency reg Default is 1.0.
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--use_bn bool Using batch normalization. Default is False
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```
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## Examples
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Train a model which follows the original hyperparameters on different datasets.
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```bash
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# Cora:
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python main.py --dataname cora --gpu 0 --lam 1.0 --tem 0.5 --order 8 --sample 4 --input_droprate 0.5 --hidden_droprate 0.5 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 100 --lr 1e-2 --epochs 2000
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# Citeseer:
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python main.py --dataname citeseer --gpu 0 --lam 0.7 --tem 0.3 --order 2 --sample 2 --input_droprate 0.0 --hidden_droprate 0.2 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 100 --lr 1e-2 --epochs 2000
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# Pubmed:
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python main.py --dataname pubmed --gpu 0 --lam 1.0 --tem 0.2 --order 5 --sample 4 --input_droprate 0.6 --hidden_droprate 0.8 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 200 --lr 0.2 --epochs 2000 --use_bn
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```
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### Performance
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The hyperparameter setting in our implementation is identical to that reported in the paper.
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| Dataset | Cora | Citeseer | Pubmed |
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| :-: | :-: | :-: | :-: |
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| Accuracy Reported(100 runs) | **85.4(±0.4)** | **75.4(±0.4)** | 82.7(±0.6) |
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| Accuracy DGL(20 runs) | 85.33(±0.41) | 75.36(±0.36) | **82.90(±0.66)** |
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