Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs

2 code implementations • 7 Feb 2023 • Saro Passaro, C. Lawrence Zitnick

Graph neural networks that model 3D data, such as point clouds or atoms, are typically desired to be $SO(3)$ equivariant, i. e., equivariant to 3D rotations.

Directional Graph Networks

2 code implementations • 6 Oct 2020 • Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, Pietro Liò

Then, we propose the use of the Laplacian eigenvectors as such vector field.

Data Augmentation Graph Classification +2