This DGL example implements the GNN model proposed in the paper Graph Neural Networks with convolutional ARMA filters.
Contributor: xnuohz
The codebase is implemented in Python 3.6. For version requirement of packages, see below.
dgl
numpy 1.19.5
networkx 2.5
scikit-learn 0.24.1
tqdm 4.56.0
torch 1.7.0
The DGL's built-in Cora, Pubmed, Citeseer datasets. Dataset summary:
| Dataset | #Nodes | #Edges | #Feats | #Classes | #Train Nodes | #Val Nodes | #Test Nodes |
|---|---|---|---|---|---|---|---|
| Cora | 2,708 | 10,556 | 1,433 | 7(single label) | 140 | 500 | 1000 |
| Citeseer | 3,327 | 9,228 | 3,703 | 6(single label) | 120 | 500 | 1000 |
| Pubmed | 19,717 | 88,651 | 500 | 3(single label) | 60 | 500 | 1000 |
--dataset str The graph dataset name. Default is 'Cora'.
--gpu int GPU index. Default is -1, using CPU.
--epochs int Number of training epochs. Default is 2000.
--early-stopping int Early stopping rounds. Default is 100.
--lr float Adam optimizer learning rate. Default is 0.01.
--lamb float L2 regularization coefficient. Default is 0.0005.
--hid-dim int Hidden layer dimensionalities. Default is 16.
--num-stacks int Number of K. Default is 2.
--num-layers int Number of T. Default is 1.
--dropout float Dropout applied at all layers. Default is 0.75.
The following commands learn a neural network and predict on the test set. Train an ARMA model which follows the original hyperparameters on different datasets.
# Cora:
python citation.py --gpu 0
# Citeseer:
python citation.py --gpu 0 --dataset Citeseer --num-stacks 3
# Pubmed:
python citation.py --gpu 0 --dataset Pubmed --dropout 0.25 --num-stacks 1| Dataset | Cora | Citeseer | Pubmed |
|---|---|---|---|
| Metrics(Table 1.Node classification accuracy) | 83.4±0.6 | 72.5±0.4 | 78.9±0.3 |
| Metrics(PyG) | 82.3±0.5 | 70.9±1.1 | 78.3±0.8 |
| Metrics(DGL) | 80.9±0.6 | 71.6±0.8 | 75.0±4.2 |