Awesome

SNS

Code for paper "Similarity-based Neighbor Selection for Graph LLMs"

Environment Setup

Assume your cuda version is 11.8

conda create --name SNS python=3.10
conda activate SNS

conda install pytorch==2.0.0 cudatoolkit=11.8 -c pytorch
conda install -c pyg pytorch-sparse
conda install -c pyg pytorch-scatter
conda install -c pyg pytorch-cluster
conda install -c pyg pyg
pip install ogb
conda install -c dglteam/label/cu118 dgl
pip install transformers
pip install --upgrade accelerate
pip install openai
pip install editdistance
pip install python-json-logger

Dataset Preparation

For cora, pubmed, ogbn-arxiv and ogbn-product, please following the dataset processing instructions in LLM-Structured-Data to download and place the data under /dataset/{datasetname}.

For citeseer, please download via this link and place it under /dataset/citeseer. This preprocessed data is borrowed from Graph-LLM.

API Settings

The api-related arguments are specified in call_api.py, including the api key. Please set up your OpenAI API in call_api.py.

Usage

python main.py  [--dataset DATASET] [--mode MODE] [--k K]

Arguments

• --dataset: Specifies the name of dataset. Default is "cora". Choices are ["cora", "pubmed", "citeseer" , "arxiv", "product"]. "arxiv" and "product" represent ogbn-arxiv and ogbn-products respectively.
• --mode: Specifies the mode of this execution. Default is "tl". Choices are ['t', 'l', 'tl', "none"]. "t" represents text, while "l" represents label. "tl" represents text+label, and "none" represents vanilla zero-shot, where we don't incorporate graph information.
• --k: Specifies the number of neighbors being leveraged. Default is -1, which directly use the k specified in our paper.

Examples

Run SNS for the cora dataset in text+label mode and default k settings:

python main.py --dataset cora --mode tl --k -1 

Run SNS for the ogbn-arxiv dataset in text only mode and k=8 setting:

python main.py --dataset arxiv --mode t --k 8 

Citation

If you find the above code is helpful for your research, please cite our paper.

@article{li2024similarity,
  title={Similarity-based Neighbor Selection for Graph LLMs},
  author={Li, Rui and Li, Jiwei and Han, Jiawei and Wang, Guoyin},
  journal={arXiv preprint arXiv:2402.03720},
  year={2024}
}