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<h1 align="center"> ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs </a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update.</h5> <h5 align="center">

GitHub stars

</h5>

This is the official implementation of the following paper:

ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs [Paper]

Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, Jia Li

<p align="center"><img width="75%" src="ZeroG.png" /></p> <p align="center"><em>The framework of ZeroG.</em></p>

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, while other datasets in ZeroG are available in this link. Please download and place them in folder datasets.

Run ZeroG

bash run.sh

<br> 📑 If you find our projects helpful to your research, please consider citing: <br>

@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

<p align="center"><em>🔥 <strong>A Survey of Graph Meets Large Language Model: Progress and Future Directions (IJCAI'24) <img src="https://img.shields.io/github/stars/yhLeeee/Awesome-LLMs-in-Graph-tasks.svg" alt="GitHub stars" /></strong></em></p> <p align="center"><em><a href="https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks">Github Repo</a> | <a href="https://arxiv.org/abs/2311.12399">Paper</a></em></p>