Releases: sheldonresearch/ProG
v 1.0
ProGv0.2 released!
Big News！ We are so happy to announce that we have finished most updating works from ProG to ProG++!
From v0.2, ProG means ProG++
🌟ProG++🌟: A Unified Python Library for Graph Prompting
ProG++ is an extended library with ProG, which supports more graph prompt models. Some implemented models are as follows:
[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.
[GraphPrompt] Liu Z, Yu X, Fang Y, et al. Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. The Web Conference, 2023.
[GPPT] M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang, “GPPT: Graph Pre-Training and Prompt Tuning to Generalize Graph Neural Networks,” KDD, 2022
[GPF] T. Fang, Y. Zhang, Y. Yang, and C. Wang, “Prompt tuning for graph neural networks,” arXiv preprint, 2022.
ProG v0.1 released
ProG (Prompt Graph) is a library built upon PyTorch to easily conduct single or multi-task prompting for pre-trained Graph Neural Networks (GNNs). The idea is derived from the paper: Xiangguo Sun, Hong Cheng, Jia Li, etc. All in One: Multi-task Prompting for Graph Neural Networks. KDD2023 (🔥 Best Research Paper Award, which is the first time for Hong Kong and Mainland China), in which they released their raw codes. This repository is a redesigned and enhanced version of the raw codes with extremely huge changes and updates