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sheldonresearch authored Feb 24, 2024
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<p align="left">

![](https://img.shields.io/badge/Latest_version-v0.1.5-red)
![](https://img.shields.io/badge/Latest_version-v0.1-red)
![Testing Status](https://img.shields.io/badge/docs-in_progress-green)
![Testing Status](https://img.shields.io/badge/pypi_package-in_progress-green)
![Testing Status](https://img.shields.io/badge/PyTorch-v1.13.1-red)
Expand All @@ -29,17 +29,22 @@

</p>

<h3 align="center">

Big News！ We are so happy to announce that we have finished most updating works from ProG to ProG++!
<h3>

(if you wish to find the original ProG package, go to prog branch)
![](https://img.shields.io/badge/News-red)
Big News！

</h3>

<h3>🌟ProG++🌟: A Unified Python Library for Graph Prompting</h3>
- We are so happy to announce that we have finished most updating works from ProG to **ProG++**! (the ``main`` branch of this repository. If you wish to find the original ProG package, go to the ``ori`` branch)


---


<h3 align="center">🌟ProG++🌟: A Unified Python Library for Graph Prompting</h3>

**ProG++** is an extended library with **ProG**, which supports more graph prompt models. Currently, **ProG++** is now in its beta version (a little baby: [ProG Plus](https://github.com/Barristen/Prog_plus)), and we will merge ``ProG Plus`` to ``ProG`` in the near future. Some implemented models are as follows (_We are now implementing more related models and we will keep integrating more models to ProG++_):
**ProG++** (the ``main`` branch of this repository) is an extended library of the original ``ProG`` (see in the ``ori`` branch of this repository), which supports more graph prompt models. Some implemented models are as follows (_We are now implementing more related models and we will keep integrating more models to ProG++_):

>- [All in One] X. Sun, H. Cheng, J. Li, B. Liu, and J. Guan, “All in One: Multi-Task Prompting for Graph Neural Networks,” KDD, 2023
>- [GPF Plus] T. Fang, Y. Zhang, Y. Yang, C. Wang, and L. Chen, “Universal Prompt Tuning for Graph Neural Networks,” NeurIPS, 2023.
Expand All @@ -50,10 +55,9 @@ Big News！ We are so happy to announce that we have finished most updating work

<br>

<h3 align="center">

![](https://img.shields.io/badge/News-red)
🐶We released a comprehensive survey on graph prompt!
<h3>

We released a comprehensive survey on graph prompt!

</h3>

Expand Down Expand Up @@ -272,34 +276,6 @@ this version code | 79.78 ｜ 80.01 ｜

- The above table is copied from this blog: https://github.com/sheldonresearch/ProG/blob/main/History.md#13-jul-2023

## Citation

bibtex

```
@inproceedings{sun2023all,
title={All in One: Multi-Task Prompting for Graph Neural Networks},
author={Sun, Xiangguo and Cheng, Hong and Li, Jia and Liu, Bo and Guan, Jihong},
booktitle={Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining (KDD'23)},
year={2023},
pages = {2120–2131},
location = {Long Beach, CA, USA},
isbn = {9798400701030},
url = {https://doi.org/10.1145/3580305.3599256},
doi = {10.1145/3580305.3599256}
}
```
```
@article{sun2023graph,
title = {Graph Prompt Learning: {{A}} Comprehensive Survey and Beyond},
author = {Sun, Xiangguo and Zhang, Jiawen and Wu, Xixi and Cheng, Hong and Xiong, Yun and Li, Jia},
year = {2023},
journal = {arXiv:2311.16534},
eprint = {2311.16534},
archiveprefix = {arxiv}
}
```


## Contact
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<h1 align='left'>
ProG Plus (Updating)
</h1>


<h5 align="left">

![](https://img.shields.io/badge/Latest_version-v0.1.5-red)
![Testing Status](https://img.shields.io/badge/docs-in_progress-green)
![Testing Status](https://img.shields.io/badge/pypi_package-in_progress-green)
![Testing Status](https://img.shields.io/badge/PyTorch-v2.0.1-red)
![Testing Status](https://img.shields.io/badge/license-MIT-blue)
![Testing Status](https://img.shields.io/badge/python->=3.9-red)

</h5>

<br>

🌟 ``ProG Plus`` is a baby of **ProG++**, an extended library upon [![](https://img.shields.io/badge/ProG-red)](https://github.com/sheldonresearch/ProG). ``ProG Plus`` supports more graph prompt models, and we will merge ``ProG Plus`` to [![](https://img.shields.io/badge/ProG-red)](https://github.com/sheldonresearch/ProG) in the near future (named as **ProG++**). Some implemented models are as follows (_We are now implementing more related models and we will keep integrating more models to ProG++_):
>- ( _**KDD23 Best Paper**_ 🌟) X. Sun, H. Cheng, J. Li, B. Liu, and J. Guan, “All in One: Multi-Task Prompting for Graph Neural Networks,” in KDD, 2023
>- M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang, “GPPT: Graph Pre-Training and Prompt Tuning to Generalize Graph Neural Networks,” in KDD, 2022
>- T. Fang, Y. Zhang, Y. Yang, and C. Wang, “Prompt tuning for graph neural networks,” arXiv preprint, 2022.
>- T. Fang, Y. Zhang, Y. Yang, C. Wang, and L. Chen, “Universal Prompt Tuning for Graph Neural Networks,” in NeurIPS, 2023.

<h5 align='center'>

Thanks to Dr. Xiangguo Sun for his

[![](https://img.shields.io/badge/Python_Library-ProG-red)](https://github.com/sheldonresearch/ProG)

Please visit their [website](https://github.com/sheldonresearch/ProG) to inquire more details on **ProG**, **ProG Plus**, and **ProG++**

</h5>

## TODO List

> **Note**
Expand Down Expand Up @@ -465,72 +400,3 @@ or run this command
```shell
conda install pyg -c pyg
```
## Usage

See in [https://github.com/sheldonresearch/ProG](https://github.com/sheldonresearch/ProG)

## Citation

bibtex

```
@inproceedings{sun2023all,
title={All in One: Multi-Task Prompting for Graph Neural Networks},
author={Sun, Xiangguo and Cheng, Hong and Li, Jia and Liu, Bo and Guan, Jihong},
booktitle={Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining (KDD'23)},
year={2023},
pages = {2120–2131},
location = {Long Beach, CA, USA},
isbn = {9798400701030},
url = {https://doi.org/10.1145/3580305.3599256},
doi = {10.1145/3580305.3599256}
}
@article{sun2023graph,
title = {Graph Prompt Learning: A Comprehensive Survey and Beyond},
author = {Sun, Xiangguo and Zhang, Jiawen and Wu, Xixi and Cheng, Hong and Xiong, Yun and Li, Jia},
year = {2023},
journal = {arXiv:2311.16534},
eprint = {2311.16534},
archiveprefix = {arxiv}
}
@article{zhao2024all,
title={All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining},
author={Haihong Zhao and Aochuan Chen and Xiangguo Sun and Hong Cheng and Jia Li},
year={2024},
eprint={2402.09834},
archivePrefix={arXiv}
}
@inproceedings{gao2024protein,
title={Protein Multimer Structure Prediction via {PPI}-guided Prompt Learning},
author={Ziqi Gao and Xiangguo Sun and Zijing Liu and Yu Li and Hong Cheng and Jia Li},
booktitle={The Twelfth International Conference on Learning Representations (ICLR)},
year={2024},
url={https://openreview.net/forum?id=OHpvivXrQr}
}
@article{chen2024prompt,
title={Prompt Learning on Temporal Interaction Graphs},
author={Xi Chen and Siwei Zhang and Yun Xiong and Xixi Wu and Jiawei Zhang and Xiangguo Sun and Yao Zhang and Yinglong Zhao and Yulin Kang},
year={2024},
eprint={2402.06326},
archivePrefix={arXiv},
journal = {arXiv:2402.06326}
}
@article{li2024survey,
title={A Survey of Graph Meets Large Language Model: Progress and Future Directions},
author={Yuhan Li and Zhixun Li and Peisong Wang and Jia Li and Xiangguo Sun and Hong Cheng and Jeffrey Xu Yu},
year={2024},
eprint={2311.12399},
archivePrefix={arXiv},
journal = {arXiv:2311.12399}
}
```

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