Awesome
GMPT
Official PyTorch implementation for Graph Matching based GNN Pre-Training.
Yupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, Ji-Rong Wen. Neural Graph Matching for Pre-training Graph Neural Networks. SDM 2022.
Overview
Requirements
python 3.7.7
pytorch 1.7.1
torch-geometric 1.6.3
cudatoolkit 10.1
rdkit 2020.09.1.0
Quick Start
Fine-tune with pre-trained GMPT-CL model on Bio
bash scripts/bio.sh
Pre-train from scratch
bash scripts/bio.sh pretrain
Check the results
python result_analysis.py --mode bio
For more detailed and customized usage, e.g., change datasets, GNN types, pre-trained models et al., please kindly refer to bio.sh and chem.sh.
Datasets
Please refer to dataset-download to download Bio
and Chem
datasets.
Then the downloaded datasets should be moved to dataset/
.
Pre-trained models
You can download the pre-trained GNN models from Google Drive and move them to bio_pretrain_model
and chem_pretrain_model
.
Acknowledgement
The implementation is reference to pretrain-gnns (backbone) and GraphCL (augmentation).
If you use this code for your research, please cite the following paper.
@inproceedings{hou2022gmpt,
author = {Yupeng Hou and Binbin Hu and Wayne Xin Zhao and Zhiqiang Zhang and Jun Zhou and Ji-Rong Wen},
title = {Neural Graph Matching for Pre-training Graph Neural Networks},
booktitle = {{SDM}},
year = {2022}
}