ytchx1999 / LAGNN

ICML'22: Local Augmentation for Graph Neural Networks

Local Augmentation for Graph Neural Networks

This repository contains an implementation of "Local Augmentation for Graph Neural Networks".

Dependencies

• CUDA 10.2.89
• python 3.6.8
• pytorch 1.9.0
• pyg 2.0.3

Usage

• For semi-supervised setting, run the following script

cd Citation
bash semi.sh

• For full-supervised setting, run the following script

cd OGB
# If you want to pre-train the generative model, run the following command:
python cvae_generate_products.py --latent_size 10 --pretrain_lr 1e-5 --total_iterations 10000 --batch_size 8192
python cvae_generate_arxiv.py --latent_size 10 --pretrain_lr 1e-5 --total_iterations 10000 --batch_size 8192
python cvae_generate_proteins.py --latent_size 10 --pretrain_lr 1e-5 --total_iterations 10000 --batch_size 8192
# Train downstream GNNs
bash full.sh

Citation

@inproceedings{liu2022local,
  title={Local augmentation for graph neural networks},
  author={Liu, Songtao and Ying, Rex and Dong, Hanze and Li, Lanqing and Xu, Tingyang and Rong, Yu and Zhao, Peilin and Huang, Junzhou and Wu, Dinghao},
  booktitle={International Conference on Machine Learning},
  year={2022}
}