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GCPA

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Adapting Precomputed Features for Efficient Graph Condensation (ICML 2025)

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# GCPA

Source code (PyTorch) and dataset of the paper "[Adapting Precomputed Features for Efficient Graph Condensation](https://openreview.net/forum?id=ThK6o74QLc)", which is accepted by Forty-second International Conference on Machine Learning (ICML 2025).

## Implementation and Paper Link

+ Implementation: [https://github.com/Xtra-Computing/GCPA](https://github.com/Xtra-Computing/GCPA)
+ Paper Access: [https://openreview.net/forum?id=ThK6o74QLc](https://openreview.net/forum?id=ThK6o74QLc)

## Requirements

I have tested this environment with the following requirements, but please note that you are not restricted to these specific versions and can choose other versions as appropriate:

- python=3.10.13
- torch=2.4.1
- dgl=2.4.0
- ogb=1.3.6
- numpy=1.26.4
- scikit-learn=1.6.1
- wandb=0.19.7

## Run GCPA

```bash
./run.sh
```

## Cite

If you use GCPA in a scientific publication, we would appreciate citations to the following paper:

```
@inproceedings{
    li2025adapting,
    title={Adapting Precomputed Features for Efficient Graph Condensation},
    author={Yuan Li and Jun Hu and Zemin Liu and Bryan Hooi and Jia Chen and Bingsheng He},
    booktitle={Forty-second International Conference on Machine Learning},
    year={2025},
    url={https://openreview.net/forum?id=ThK6o74QLc}
}
```

__License:__ [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html)

GCPA

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