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Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification (NeurIPS 2024)

OpenReview arXiv

PWC PWC

Python environment setup with Conda

Tested with Python 3.7, PyTorch 1.12.1, and PyTorch Geometric 2.3.1, dgl 1.0.2.

pip install pandas
pip install scikit_learn
pip install numpy
pip install scipy
pip install einops
pip install ogb
pip install pyyaml
pip install googledrivedownloader
pip install networkx
pip install gdown
pip install matplotlib

Overview

  • ./medium_graph Experiment code on medium graphs.

  • ./large_graph Experiment code on large graphs.

Reference

If you find our codes useful, please consider citing our work

@inproceedings{
luo2024classic,
title={Classic {GNN}s are Strong Baselines: Reassessing {GNN}s for Node Classification},
author={Yuankai Luo and Lei Shi and Xiao-Ming Wu},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=xkljKdGe4E}
}

Poster

gnn-min.png

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Pytorch Lightning (& Pytorch Geometric) Implementation of "Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification"

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  • Python 92.8%
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