xiangsheng1325 / fastgae_pytorch

A PyTorch implementation of FastGAE by Guillaume Salha.

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)