Self-Supervised Graph Representation Learning via Topology Transformations
This repository is the official PyTorch implementation of the following paper:
Xiang Gao, Wei Hu, Guo-Jun Qi, “Self-Supervised Graph Representation Learning via Topology Transformations,” IEEE Transactions on Knowledge and Data Engineering (TKDE), December 2021.
Requirements
- Python3>=3.7.10
- pytorch>=1.9.0
- tensorboardX>=1.9
- torch_geometric>=1.7.2
Note: We are not sure whether the code can be run properly by using a lower version of the relevant package.
Usage
Tip: Experimental results of graphs usually face greater randomness than images. We suggest you run the experiment more than one time and select the best result.
Unsupervised Training
To train a feature extractor in an unsupervised fashion, run
python main.py --phase backbone --dataset cora --perturbation-rate 0.7 --hidden-channels 512 --k 2 --num-epochs 512 --lr 0.0001 --use-cuda true --device 0 --save-dir ./results
Supervised Evaluation
After training the feature extractor, you need to train the classifier by running the following command:
python main.py --phase classifier --dataset cora --perturbation-rate 0.7 --hidden-channels 512 --k 2 --backbone ./results/cora_best.pt --lr 0.001 --use-cuda true --device 0 --save-dir ./results
Reference
Please cite our paper if you use any part of the code from this repository:
@article{gao2021topoter,
title={Self-Supervised Graph Representation Learning via Topology Transformations},
author={Gao, Xiang and Hu, Wei and Qi, Guo-Jun},
journal={IEEE Transactions on Knowledge and Data Engineering (TKDE)},
month={December},
year={2021}
}
Acknowledgement
Our code is released under MIT License (see LICENSE for details). Some of the code in this repository was borrowed from the following repositories:
