FastGAE in Pytorch

This repository implements FastGAE by Guillaume Salha For details of the model, refer to his original tensorflow implementation and his paper.

Requirements

• Pytorch
• python 3.x
• networkx
• scikit-learn
• scipy

How to run

• Specify your arguments in args.py : you can change dataset and other arguments there
• run python train_fastgae.py

How to run faster and save memory

• Change your arguments in args.py : you can lower the value of emb_size and sample_size to reduce memory.

Notes

• The dataset is the same as what Guillaume provided in his original implementation.
• Per-epoch training time is a bit slower then the original implementation.
• Dynamic updates of pos_weight before calculating loss are implemented to improve performance.
• Train accuracy, validation(test) average precision, auroc are not implemented due to time limit.
• Dropout is not implemented now.
• Pair-Normalization is implemented to accelerate the training.
• Feel free to report some inefficiencies in the code! (It's just initial version so may have much room for pytorch-adaptation)