grand

Graph Random Neural Network(GRAND)

This DGL example implements the GNN model proposed in the paper Graph Random Neural Network for Semi-Supervised Learning on Graphs.

Author's code: https://github.com/THUDM/GRAND

Example Implementor

This example was implemented by Hengrui Zhang when he was an applied scientist intern at AWS Shanghai AI Lab.

Dependencies

• Python 3.7
• PyTorch 1.7.1
• dgl 0.5.3

Dataset

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

Arguments

Dataset options

--dataname          str     The graph dataset name.             Default is 'cora'.

GPU options

--gpu              int     GPU index.                          Default is -1, using CPU.

Model options

--epochs           int     Number of training epochs.             Default is 2000.
--early_stopping   int     Early stopping patience rounds.        Default is 200.
--lr               float   Adam optimizer learning rate.          Default is 0.01.
--weight_decay     float   L2 regularization coefficient.         Default is 5e-4.
--dropnode_rate    float   Dropnode rate (1 - keep probability).  Default is 0.5.
--input_droprate   float   Dropout rate of input layer.           Default is 0.5.
--hidden_droprate  float   Dropout rate of hidden layer.          Default is 0.5.
--hid_dim          int     Hidden layer dimensionalities.         Default is 32.
--order            int     Propagation step.                      Default is 8.
--sample           int     Sampling times of dropnode.            Default is 4.
--tem              float   Sharpening temperaturer.               Default is 0.5.
--lam              float   Coefficient of Consistency reg         Default is 1.0.
--use_bn           bool    Using batch normalization.             Default is False

Examples

Train a model which follows the original hyperparameters on different datasets.

# Cora:
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
# Citeseer:
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
# Pubmed:
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

Performance

The hyperparameter setting in our implementation is identical to that reported in the paper.

Dataset	Cora	Citeseer	Pubmed
Accuracy Reported(100 runs)	85.4(±0.4)	75.4(±0.4)	82.7(±0.6)
Accuracy DGL(20 runs)	85.33(±0.41)	75.36(±0.36)	82.90(±0.66)