This is a Pytorch implementation of the following paper:
IJCAI22-Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
If you make use of the code/experiment in your work, please cite our paper (Bibtex below).
@inproceedings{ijcai2022p518,
title = {Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport},
author = {Zhang, Jiying and Xiao, Xi and Huang, Long-Kai and Rong, Yu and Bian, Yatao},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {3730--3736},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/518},
url = {https://doi.org/10.24963/ijcai.2022/518},
}
We would like to appreciate the excellent work of Pretrain-GNNs and TransferLearningLibrary, which both lay a solid foundation for our work.
We used the following Python packages for core development. We tested on Python 3.7.6
.
pytorch 1.4.0
torch-geometric 1.6.0
torch-scatter 2.0.4
torch-sparse 0.6.1
torch-spline-conv 1.2.0
rdkit 2020.03.3.0
tqdm 4.42.1
tensorboardx 2.1
All the necessary data files can be downloaded from the following links.
For the chemistry dataset, download from http://snap.stanford.edu/gnn-pretrain/data/chem_dataset.zip (2.5GB), unzip it, and put it under chem/dataset
.
sh chem/run.sh
The pre-trained models are under model_gin/
and model_architecture/
, copied from Pretrain-GNNs .