[ICML24] Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
pip install -r requirements.txt
Before executing following shell, please make sure that you specify the right data_directory
and log_directory
based on your machine. After the Training and Evaluating Ends, a log file will be generated in the log_directory, containing args, and losses.
1. N-body System Dataset
Data Generation:
cd ./datasets/nbody/datagen
bash run.sh
cd ../../..
Train and Evaluate Model:
bash run_nbody.sh
2. Protein Molecular Dynamics Dataset
Train and Evaluate Model:
bash run_protein.sh
The Dataset will automatically download in the directory you specified.
3. Water-3D Dataset
Follow the instruction introduced here to download the .tfrecord format Water-3D data and transform them to .h5. Place the .h5 format data in your data directory.
Train and Evaluate Model:
bash run_simulation.sh
4. QM9 Dataset
Download this dataset from link and place it in your data directory.
Train and Evaluate Model:
bash run_simulation.sh
python equivariant.py
It will random generate a graph G
, rotation matrix R
and translation vector t
, and check FastEGNN(G @ R + t)
equals to FastEGNN(G) @ R + t
or not.
If you find our work helpful, please cite as:
@inproceedings{
zhang2024improving,
title={Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning},
author={Yuelin Zhang and Jiacheng Cen and Jiaqi Han and Zhiqiang Zhang and JUN ZHOU and Wenbing Huang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=wWdkNkUY8k}
}