Graphormer

This repo is the official implementation of "Do Transformers Really Perform Badly for Graph Representation?".

News

12/10/2021

Graphormer has won the Open Catalyst Challenge. The technical talk could be found through this link.
The slides of NeurIPS 2021 could be found through this link.
The new release of Graphormer is comming soon, as a general molecule modeling toolkit, with models used in OC dataset, completed pre-trained model zoo, flexible data interface, and higher effiency of training.

09/30/2021

Graphormer has been accepted by NeurIPS 2021.
We're hiring! Please contact shuz[at]microsoft.com for more information.

08/03/2021

Codes and scripts are released.

06/16/2021

Graphormer has won the 1st place of quantum prediction track of Open Graph Benchmark Large-Scale Challenge (KDD CUP 2021) [Competition Description] [Competition Result] [Technical Report] [Blog (English)] [Blog (Chinese)]

Introduction

Graphormer is initially described in arxiv, which is a standard Transformer architecture with several structural encodings, which could effectively encoding the structural information of a graph into the model.

Graphormer achieves strong performance on PCQM4M-LSC (0.1234 MAE on val), MolPCBA (31.39 AP(%) on test), MolHIV (80.51 AUC(%) on test) and ZINC (0.122 MAE on test), surpassing previous models by a large margin.

Main Results

PCQM4M-LSC

Method	#params	train MAE	valid MAE
GCN	2.0M	0.1318	0.1691
GIN	3.8M	0.1203	0.1537
GCN-VN	4.9M	0.1225	0.1485
GIN-VN	6.7M	0.1150	0.1395
Graphormer-Small	12.5M	0.0778	0.1264
Graphormer	47.1M	0.0582	0.1234

OGBG-MolPCBA

Method	#params	test AP (%)
DeeperGCN-VN+FLAG	5.6M	28.42
DGN	6.7M	28.85
GINE-VN	6.1M	29.17
PHC-GNN	1.7M	29.47
GINE-APPNP	6.1M	29.79
Graphormer	119.5M	31.39

OGBG-MolHIV

Method	#params	test AP (%)
GCN-GraphNorm	526K	78.83
PNA	326K	79.05
PHC-GNN	111K	79.34
DeeperGCN-FLAG	532K	79.42
DGN	114K	79.70
Graphormer	47.0M	80.51

ZINC-500K

Method	#params	test MAE
GIN	509.5K	0.526
GraphSage	505.3K	0.398
GAT	531.3K	0.384
GCN	505.1K	0.367
GT	588.9K	0.226
GatedGCN-PE	505.0K	0.214
MPNN (sum)	480.8K	0.145
PNA	387.2K	0.142
SAN	508.6K	0.139
Graphormer-Slim	489.3K	0.122

Requirements and Installation

Setup with Conda

# create a new environment
conda create --name graphormer python=3.7
conda activate graphormer
# install requirements
pip install rdkit-pypi cython
pip install ogb==1.3.1 pytorch-lightning==1.3.0
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch-geometric==1.6.3 ogb==1.3.1 pytorch-lightning==1.3.1 tqdm torch-sparse==0.6.9 torch-scatter==2.0.6 -f https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html

Citation

Please kindly cite this paper if you use the code:

@inproceedings{
ying2021do,
title={Do Transformers Really Perform Badly for Graph Representation?},
author={Chengxuan Ying and Tianle Cai and Shengjie Luo and Shuxin Zheng and Guolin Ke and Di He and Yanming Shen and Tie-Yan Liu},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=OeWooOxFwDa}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

jk3472/Graphormer