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https://github.com/graphdeeplearning/benchmarking-gnns
Repository for benchmarking graph neural networks
https://github.com/graphdeeplearning/benchmarking-gnns
benchmark-framework deep-learning dgl graph-deep-learning graph-neural-networks graph-representation-learning pytorch
Last synced: 6 days ago
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Repository for benchmarking graph neural networks
- Host: GitHub
- URL: https://github.com/graphdeeplearning/benchmarking-gnns
- Owner: graphdeeplearning
- License: mit
- Created: 2020-03-03T03:42:50.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-06-22T04:03:53.000Z (about 1 year ago)
- Last Synced: 2024-02-29T23:39:15.322Z (4 months ago)
- Topics: benchmark-framework, deep-learning, dgl, graph-deep-learning, graph-neural-networks, graph-representation-learning, pytorch
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/2003.00982
- Size: 3.21 MB
- Stars: 2,379
- Watchers: 59
- Forks: 439
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - graphdeeplearning/benchmarking-gnns - Repository for benchmarking graph neural networks (Jupyter Notebook)
- awesome-stars - benchmarking-gnns
- awesome-stars - graphdeeplearning/benchmarking-gnns - Repository for benchmarking graph neural networks (Jupyter Notebook)
- awesome - Benchmarking Graph Neural Networks
- awesome-list - benchmarking-gnns - Repository for benchmarking graph neural networks. (Graph / Others)
README
# Benchmarking Graph Neural Networks
## Updates
**May 10, 2022**
* Project based on DGL 0.6.1 and higher. See the relevant dependencies defined in the environment yml files ([CPU](./environment_cpu.yml), [GPU](./environment_gpu.yml)).
* Updated technical report of the framework on [ArXiv](https://arxiv.org/pdf/2003.00982.pdf).
* Added [AQSOL dataset](https://www.nature.com/articles/s41597-019-0151-1), 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](https://github.com/graphdeeplearning/benchmarking-gnns/issues/57).
**Oct 7, 2020**
* Repo updated to DGL 0.5.2 and PyTorch 1.6.0. Please update your environment using yml files ([CPU](./environment_cpu.yml), [GPU](./environment_gpu.yml)).
* Added [ZINC-full](./data/script_download_molecules.sh) dataset (249K molecular graphs) with [scripts](./scripts/ZINC-full/).
**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](https://github.com/graphdeeplearning/benchmarking-gnns/tree/arXivV1).
* New ArXiv's version of the [paper](https://arxiv.org/pdf/2003.00982.pdf).
**Mar 3, 2020**
* First release of the project.
## 1. Benchmark installation
[Follow these instructions](./docs/01_benchmark_installation.md) to install the benchmark and setup the environment.
## 2. Download datasets
[Proceed as follows](./docs/02_download_datasets.md) to download the benchmark datasets.
## 3. Reproducibility
[Use this page](./docs/03_run_codes.md) to run the codes and reproduce the published results.
## 4. Adding a new dataset
[Instructions](./docs/04_add_dataset.md) to add a dataset to the benchmark.
## 5. Adding a Message-passing GCN
[Step-by-step directions](./docs/05_add_mpgcn.md) to add a MP-GCN to the benchmark.
## 6. Adding a Weisfeiler-Lehman GNN
[Step-by-step directions](./docs/06_add_wlgnn.md) to add a WL-GNN to the benchmark.
## 7. Leaderboards
Full leaderboards coming soon on [paperswithcode.com](https://paperswithcode.com/paper/benchmarking-graph-neural-networks).
## 8. Reference
[ArXiv's paper](https://arxiv.org/pdf/2003.00982.pdf)
```
@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}
}
```