ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs

If you like our project, please give us a star ⭐ on GitHub for the latest update.

  

This is the official implementation of the following paper: > **ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs** [[Paper](https://arxiv.org/abs/2402.11235)] > > Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, Jia Li The framework of ZeroG. # Environment Setup Before you begin, ensure that you have Anaconda or Miniconda installed on your system. This guide assumes that you have a CUDA-enabled GPU. After create your conda environment (we recommend python==3.10), please run

pip install -r requirements.txt

to install python packages. # Datasets Datasets ```tech.pt``` and ```home.pt``` are availabel in this [link](https://drive.google.com/drive/folders/1kifRUaZ9JzcjByj47FeANfXEeEZd4sJk), while other datasets in ZeroG are available in this [link](https://drive.google.com/drive/folders/1WfBIPA3dMd8qQZ6QlQRg9MIFGMwnPdFj?usp=drive_link). Please download and place them in folder ```datasets```. # Run ZeroG

bash run.sh

**📑 If you find our projects helpful to your research, please consider citing:**

@article{li2024zerog,
  title={ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs},
  author={Li, Yuhan and Wang, Peisong and Li, Zhixun and Yu, Jeffrey Xu and Li, Jia},
  journal={arXiv preprint arXiv:2402.11235},
  year={2024}
}

## FYI: our other works 🔥 A Survey of Graph Meets Large Language Model: Progress and Future Directions (IJCAI'24) Github Repo | Paper