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GCPA

Source code (PyTorch) and dataset of the paper "Adapting Precomputed Features for Efficient Graph Condensation", which is accepted by Forty-second International Conference on Machine Learning (ICML 2025).

Implementation and Paper Link

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

./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

Copyright (c) 2024-2025 Xtra Computing Group, NUS, Singapore.

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Adapting Precomputed Features for Efficient Graph Condensation (ICML 2025)

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