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Implementations of methods proposed in the paper "Conformal Prediction Sets for Graph Neural Networks"

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Reproduce the experiments

  1. Create a new environment with the following dependencies:
    • Pytorch :
      pip install torch torchvision torchaudio
    • Pytorch geometric, according to your Pytorch version and CUDA version:
      pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-{pytorch_version}+{cuda_version}.html
    • Seaborn:
      pip install seaborn
    • Matplotlib:
      pip install matplotlib
    • Pandas:
      pip install pandas
    • torch-conformal package, from the root of the repository, run:
      python setup.py install
  2. Run a Jupyter kernel on the environment
  3. Run the notebooks to reproduce the experiments

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