Awesome
Benchmarking Graph Neural Networks
<br>Updates
May 10, 2022
- Project based on DGL 0.6.1 and higher. See the relevant dependencies defined in the environment yml files (CPU, GPU).
- Updated technical report of the framework on ArXiv.
- Added AQSOL dataset, which is similar to ZINC for graph regression task, but has a real-world measured chemical target.
- Added mathematical datasets -- GraphTheoryProp and CYCLES which are useful to test GNNs on specific theoretical graph properties.
- Fixed issue #57.
Oct 7, 2020
- Repo updated to DGL 0.5.2 and PyTorch 1.6.0. Please update your environment using yml files (CPU, GPU).
- Added ZINC-full dataset (249K molecular graphs) with scripts.
Jun 11, 2020
- Second release of the project. Major updates :
- Added experimental pipeline for Weisfeiler-Lehman-GNNs operating on dense rank-2 tensors.
- Added a leaderboard for all datasets.
- Updated PATTERN dataset.
- Fixed bug for PATTERN and CLUSTER accuracy.
- Moved first release to this branch.
- New ArXiv's version of the paper.
Mar 3, 2020
- First release of the project.
1. Benchmark installation
Follow these instructions to install the benchmark and setup the environment.
<br>2. Download datasets
Proceed as follows to download the benchmark datasets.
<br>3. Reproducibility
Use this page to run the codes and reproduce the published results.
<br>4. Adding a new dataset
Instructions to add a dataset to the benchmark.
<br>5. Adding a Message-passing GCN
Step-by-step directions to add a MP-GCN to the benchmark.
<br>6. Adding a Weisfeiler-Lehman GNN
Step-by-step directions to add a WL-GNN to the benchmark.
<br>7. Leaderboards
Full leaderboards coming soon on paperswithcode.com.
<br>8. Reference
@article{dwivedi2020benchmarkgnns,
title={Benchmarking Graph Neural Networks},
author={Dwivedi, Vijay Prakash and Joshi, Chaitanya K and Luu, Anh Tuan and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
journal={arXiv preprint arXiv:2003.00982},
year={2020}
}
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