@article{guan2025attention,
title={Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data},
author={Guan, Zhong and Wu, Likang and Zhao, Hongke and He, Ming and Fan, Jianpin},
journal={arXiv preprint arXiv:2505.02130},
year={2025}
}
conda create --name Exploration python=3.8
conda activate Exploration
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1+cu118 -f https://download.pytorch.org/whl/torch_stable.html
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
pip install ogb
pip install yacs
pip install transformers==4.31.0
pip install peft==0.4.0
pip install accelerate
pip install bitsandbytes==0.39.0
#pip install sentence_transformers #sentence_transformers-3.0.1 tokenizers-0.19.1 transformers-4.44.2
pip install wheel
pip install torch_geometric
pip install SentencePiece
pip install protobuf
pip install bitsandbytes==0.39.0
pip install matplotlib==3.6.1
pip install protobuf==3.19.6
pip install accelerate==0.33.0
pip install bitsandbytes==0.39.0
pip install dataset==1.6.2
pip install datasets==2.21.0
We provide both the raw text data and processed embeddings of our collected datasets on Google Drive. You can download them from:
Google Drive Link
- LLM_atten_initial.py
Standard script for LLM processing of graph tasks - LLM_atten_pad.py
Enhanced script with padding and shuffle operations - LLM_atten_atten.py
Extended script with additional attention mechanisms
- LLM_cengji_A.ipynb
Exploration code for Section A of the paper, including visualization - LLM_cengji_B.ipynb
Exploration code for Section B of the paper, including visualization
We welcome contributions! Feel free to open an issue if you have any questions.
