This DGL example implements the GNN model proposed in the paper GeniePath: Graph Neural Networks with Adaptive Receptive Paths.
This example was implemented by Kay Liu during his SDE intern work at the AWS Shanghai AI Lab.
- Python 3.7.10
- PyTorch 1.8.1
- dgl 0.7.0
- scikit-learn 0.23.2
The datasets used for node classification are Pubmed citation network dataset (tranductive) and Protein-Protein Interaction dataset (inductive).
If want to train on Pubmed (transductive), run
python pubmed.py
If want to use a GPU, run
python pubmed.py --gpu 0
If want to train GeniePath-Lazy, run
python pubmed.py --lazy True
If want to train on PPI (inductive), run
python ppi.py
Dataset: Pubmed (ACC)
| Method | GeniePath |
|---|---|
| Paper | 78.5% |
| DGL | 73.0% |
Dataset: PPI (micro-F1)
| Method | GeniePath | GeniePath-lazy | GeniePath-lazy-residual |
|---|---|---|---|
| Paper | 0.9520 | 0.9790 | 0.9850 |
| DGL | 0.9729 | 0.9802 | 0.9798 |