no code implementations • 2 Oct 2023 • Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, Francesco Di Giovanni
Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors.
1 code implementation • 6 Feb 2023 • Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio', Michael Bronstein
Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
no code implementations • 26 Nov 2022 • Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò
The original dDGM architecture used the Euclidean plane to encode latent features based on which the latent graphs were generated.
no code implementations • 24 Sep 2022 • Haitz Sáez de Ocáriz Borde, Federico Barbero
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks.
1 code implementation • 17 Jun 2022 • Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò
A Sheaf Neural Network (SNN) is a type of Graph Neural Network (GNN) that operates on a sheaf, an object that equips a graph with vector spaces over its nodes and edges and linear maps between these spaces.
Ranked #6 on Node Classification on Wisconsin