We provide the code (in pytorch) and datasets for our paper "GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks", which is accepted by WWW2023.
We Further extend GraphPrompt to GraphPrompt+ by enhancing the pre-training and prompting stages "Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs" which is accepted by IEEE TKDE, the code and datasets are publicly available (https://github.com/gmcmt/graph_prompt_extension).
The repository is organised as follows:
Default dataset is ENZYMES. You need to change the corresponding parameters in pre_train.py and prompt_fewshot.py to train and evaluate on other datasets.
Pretrain:
Prompt tune and test:
Default dataset is ENZYMES. You need to change the corresponding parameters in prompt_fewshot.py to train and evaluate on other datasets.
Prompt tune and test:
@inproceedings{liu2023graphprompt,\ title={GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks},\ author={Liu, Zemin and Yu, Xingtong and Fang, Yuan and Zhang, Xinming},\ booktitle={Proceedings of the ACM Web Conference 2023},\ year={2023}\ }