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https://github.com/ArthurLeoM/FGWMixup

Code for the paper: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications, published in NeurIPS'23.
https://github.com/ArthurLeoM/FGWMixup

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Code for the paper: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications, published in NeurIPS'23.

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# FGWMixup: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications

This is the code for the paper: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications, published in NeurIPS'23.

**Paper link** 🔗:

arXiv: https://arxiv.org/abs/2306.15963

OpenReview: https://openreview.net/forum?id=uqkUguNu40&noteId=0qcp06CFB6

**Thanks for your interest in our work! If our work helps, please don't forget to cite us!🌟**

```
@inproceedings{ma2023fused,
author = {Ma, Xinyu and Chu, Xu and Wang, Yasha and Lin, Yang and Zhao, Junfeng and Ma, Liantao and Zhu, Wenwu},
booktitle = {Advances in Neural Information Processing Systems},
pages = {15252--15276},
title = {Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications},
url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/3173c427cb4ed2d5eaab029c17f221ae-Paper-Conference.pdf},
volume = {36},
year = {2023}
}
```

### File Structure

- ```./src/```: source codes

```gmixup_dgl.py```: Main python file to run FGWMixup

```gromov_mixup.py```: Conducting mixup of two samples

```FGW_barycenter.py```: Calculating FGW barycenter and its accelerated version

```models_dgl.py```: GNN architectures

```utils_dgl.py```: Some utilities

- ```run_gmixup.sh```: sh command to run FGWMixup

### Requirements
Suggested Enviornments:
- Python 3.9
- PyTorch 1.11.0
- DGL 1.0.2
- POT 0.8.2