Graph Positional and Structural Encoder

1 code implementation • 14 Jul 2023 • Renming Liu, Semih Cantürk, Olivier Lapointe-Gagné, Vincent Létourneau, Guy Wolf, Dominique Beaini, Ladislav Rampášek

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering.

Rethinking Graph Transformers with Spectral Attention

1 code implementation • NeurIPS 2021 • Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou

Here, we present the $\textit{Spectral Attention Network}$ (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph.

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