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Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"

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DIDA

Dependencies

Require

  • Python == 3.8
  • PyTorch == 1.11
  • PyTorch-Geometric == 2.0.3

Install other packages using following command at root directory of the repository

pip install -e .

Dataset

Download dataset at ./data from following links

https://drive.google.com/file/d/19SOqzYEKvkna6DKd74gcJ50Wd4phOHr3/view?usp=share_link

Usage

To run on one dataset, please execute following commands in the directory ./scripts

python main.py --dataset collab --log_dir ../logs/collab --device_id X 
python main.py --dataset yelp --log_dir ../logs/yelp --device_id X 
python main.py --dataset synthetic-0.6 --log_dir ../logs/synthetic-0.6 --device_id X 

To reproduce the main results, please execute following commands in the directory ./scripts. Change the 'resources' in the script.py as available GPU ids.

python script.py -t run
python script.py -t show

Paper

For more details, please see our paper Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift which has been accepted at NeurIPS 2022. If this code is useful for your work, please consider to cite our paper.

@inproceedings{zhang2022dynamic,
  title={Dynamic graph neural networks under spatio-temporal distribution shift},
  author={Zhang, Zeyang and Wang, Xin and Zhang, Ziwei and Li, Haoyang and Qin, Zhou and Zhu, Wenwu},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}
}

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Implementation Codes for NeurIPS22 paper "Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift"

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