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) 