GTOT-Tuning

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.

Installation

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

Dataset download

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.

Train and test

Fine-tuning with GTOT Regularization

sh chem/run.sh

Saved pre-trained models

The pre-trained models are under model_gin/ and model_architecture/, copied from Pretrain-GNNs .