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Breaking the Expressive Bottlenecks of Graph Neural Networks

The code is built upon the PyTorch Geometric.

Prerequisites

python 3.6.9

pytorch 1.5.1

pytorch-geometric 1.5.0

ogb 1.2.1

Additional modules: numpy, easydict, tensorboardx

Running the tests

Running the ogbg-ppa test.

  • cd ogbg/ppa
  • Set hyper-parameters in ogbg-ppa.json.
  • Set CUDA_VISIBLE_DEVICES and output directory in run_script.sh.
  • chmod +x run_script.sh
  • ./run_script.sh

Running the ogbg-code test.

  • cd ogbg/code
  • Set hyper-parameters in ogbg-code.json.
  • Set CUDA_VISIBLE_DEVICES and output directory in run_script.sh.
  • chmod +x run_script.sh
  • ./run_script.sh

Running the ogbg-molhiv test.

  • cd ogbg/mol
  • Set hyper-parameters in ogbg-mol.json.
  • Set CUDA_VISIBLE_DEVICES and output directory in run_script.sh.
  • chmod +x run_script.sh
  • ./run_script.sh

Running the QM9 test.

  • cd ogbg/qm9
  • Set hyper-parameters in QM9.json.
  • Set CUDA_VISIBLE_DEVICES and output directory in run_script.sh.
  • chmod +x run_script.sh
  • ./run_script.sh

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  • Python 97.4%
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