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", the code and datasets are publicly available (https://github.com/gmcmt/graph_prompt_extension).
The repository is organised as follows:
- data/: contains data we use.
- graphdownstream/: implements pre-training and downstream tasks at the graph level.
- nodedownstream/: implements downstream tasks at the node level.
- convertor/: generate raw data.
- cuda 11.3
- dgl0.9.0-cu113
- dgllife
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:
- python pre_train.py
Prompt tune and test:
- python prompt_fewshot.py
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:
- python run.py
@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}
}