Source code for the paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding", NeurIPS 2023.
The implementation of our MAP algorithm is in data/map.py, along with codes for verifying correctness and counting the ratio of assumption violations.
To download the datasets, run data/script_download_ZINC.sh. The OGBG-MOL* datasets are automatically downloaded from OGB.
To run the experiments, use the scripts in scripts/.
Attribution: Our code is built on top of the [SignNet repo] by Lim et al. in 2022, which in turn builds off of the setup in [LSPE repo] by Dwivedi et al. in 2021.
If you use our code, please cite
@inproceedings{laplacian-canonization,
title={{Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding}},
author={Ma, George and Wang, Yifei and Wang, Yisen},
booktitle={NeurIPS},
year={2023}
}