DGL Implementation of the GeniePath Paper

This DGL example implements the GNN model proposed in the paper GeniePath: Graph Neural Networks with Adaptive Receptive Paths.

Example implementor

This example was implemented by Kay Liu during his SDE intern work at the AWS Shanghai AI Lab.

Dependencies

• Python 3.7.10
• PyTorch 1.8.1
• dgl 0.7.0
• scikit-learn 0.23.2

Dataset

The datasets used for node classification are Pubmed citation network dataset (tranductive) and Protein-Protein Interaction dataset (inductive).

How to run

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

Performance

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