[ICML24] Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning

🚀 We have released an extended version of this work: Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning

🔗 👉 Click here for the new repository with updated code and experiments

We appreciate your continued interest and support!

Setup:

pip install -r requirements.txt

Run Experiments

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

Equivariant Test

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.

Citation

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}
}