31 citations · 32 across the 3 of their papers we have counts for
3 papers
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein
Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dyn…
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero +3
Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges. This inductive bias leads to a phenomenon kn…
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
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. Using meta-learning we are able…