5 papers
Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
Ferran Hernandez Caralt, Simon Heilig, Adrián Bazaga +3
Empirical claims about the connection between over-squashing and long-range interactions in GNNs, can only be trusted if the benchmarks used to validate them genuinely require long…
Cup and Cap Topological Neural Network
Marta Niedostatek, Ferran Hernandez Caralt, Runyue Wang +4
Topological Deep Learning (TDL) is designed to learn features associated with higher-dimensional simplices including not only nodes but also edges, triangles and so on. However, mo…
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2
In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classification has largely focused on a…
On the Necessity of Learnable Sheaf Laplacians
Ferran Hernandez Caralt, Mar Gonzàlez i Català, Adrián Bazaga +1
Sheaf Neural Networks (SNNs) were introduced as an extension of Graph Convolutional Networks to address oversmoothing on heterophilous graphs by attaching a sheaf to the input grap…
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
Ferran Hernandez Caralt, Guillermo Bernárdez Gil, Iulia Duta +2
Sheaf Neural Networks (SNNs) naturally extend Graph Neural Networks (GNNs) by endowing a cellular sheaf over the graph, equipping nodes and edges with vector spaces and defining li…