/VGAE_pyG

An VGAE implementation using pytorch geometric.

Primary LanguagePython

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

  1. Configure the arguments in config/vgae.yaml file. You can also make your own config file.

  2. 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