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
Last synced: 5 days ago
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Code for the paper: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications, published in NeurIPS'23.
- Host: GitHub
- URL: https://github.com/ArthurLeoM/FGWMixup
- Owner: ArthurLeoM
- Created: 2023-05-23T03:11:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-27T11:13:56.000Z (11 months ago)
- Last Synced: 2024-08-03T23:15:47.670Z (9 months ago)
- Language: Python
- Homepage:
- Size: 43.9 KB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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¬eId=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