Variational Graph Auto-encoder in Pytorch Geometric
This respository implements variational graph auto-encoder in Pytorch Geometric, adapted from the autoencoder example code in pyG. For details of the model, refer to Thomas Klpf's original paper.
Requirements
- Python >= 3.6
- Pytorch == 1.5
- Pytorch Geometric == 1.5
- scikit-learn
- scipy
How to run
-
Configure the arguments in
config/vgae.yaml
file. You can also make your own config file. -
Specify the config file and run the training script.
python train.py --load_config config/vgae.yaml
Result
We follow the arguments set as the original paper and the results is shown below.
Dataset | AUC | AP |
---|---|---|
Cora | 0.903 | 0.911 |
Citeseer | 0.869 | 0.879 |
Pubmed | 0.948 | 0.948 |