GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning

A multi-stage adaptive graph prompt selection algotithm to enhance the in-context learning over graphs.

Setup

pip install -r requirements.txt
pip install pyg_lib torch_scatter torch_sparse torch_cluster -f https://data.pyg.org/whl/torch-2.0.1+cu117.html

All datasets should be prepared to individual folders under <DATA_ROOT>. For MAG and arXiv, the datasets will be automatically downloaded and processed to <DATA_ROOT>. In case of memory issue when generating adjacency matrix, we also provide the preprocessed MAG adjacency matrix that should be put under <DATA_ROOT>/mag240m after the ogb download.

For KG, download preprocessed Wiki and FB15K-237 datasets to <DATA_ROOT>. Download other KG datasets (NELL and ConceptNet) similarly following links in https://github.com/snap-stanford/csr.

Pretraining and Evaluation

pretrain on mag240m : pretrain_mag240m.sh

pretrain on wiki : pretrain_wiki.sh

We provide pretrained model in folder ./state, you can use these models straightly.

evaluation on arxiv: eval_arxiv.sh

evaluation on ConcepNet/FB15K-237/NELL: eval_all_kg.sh

Citations

If you use this repo, please cite the following paper. This repo reuses code from CSR for KG datasets loading.

article(Under review): GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning