By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke*, Di He*, Yanming Shen and Tie-Yan Liu.

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

09/30/2021

1. Graphormer has been accepted by NeurIPS 2021.
2. Technical talk of KDD CUP 2021 could be found here. [Video]
3. We're hiring! Please contact shuz[at]microsoft.com for more information.

08/03/2021

1. Codes and scripts are released.

06/16/2021

1. 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)]

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.

# 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

Please kindly cite this paper if you use the code:

@article{ying2021transformers,
  title={Do Transformers Really Perform Bad for Graph Representation?},
  author={Ying, Chengxuan and Cai, Tianle and Luo, Shengjie and Zheng, Shuxin and Ke, Guolin and He, Di and Shen, Yanming and Liu, Tie-Yan},
  journal={arXiv preprint arXiv:2106.05234},
  year={2021}
}

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