This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to his original tensorflow implementation and his paper.
- Pytorch
- python 3.x
- networkx
- scikit-learn
- scipy
- Specify your arguments in
args.py: you can change dataset and other arguments there - run
python train.py
- The dataset is the same as what Kipf provided in his original implementation. Thus I used his preprocessing code as-is(maybe with minor modification).
- Per-epoch training time is a bit slower then the original implementation.(0.2 sec/epoch --> 0.9 sec/epoch)
- Train accuracy, validation(test) average precision, auroc are similar to those of the original. (over 90% for both AP and roc)
- Dropout is not implemented now.
- Feel free to report some inefficiencies in the code! (It's just initial version so may have much room for pytorch-adaptation)