5 papers
Diagnosing GNN Failure Modes with Layer-Wise Probe Entropy
Shanzi Bao, Mar Gonzàlez I Català, Pietro Liò
Graph neural networks (GNNs) are typically evaluated only at their final layer, obscuring how task-relevant information evolves during message passing. We introduce a post-hoc diag…
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…
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…
Learning Linear Embeddings for Non-Linear Network Dynamics with Koopman Message Passing
King Fai Yeh, Paris Flood, William Redman +1
Recently, Koopman operator theory has become a powerful tool for developing linear representations of non-linear dynamical systems. However, existing data-driven applications of Ko…
Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction
Paris D. L. Flood, Ramon Viñas, Pietro Liò
Link prediction in graphs is an important task in the fields of network science and machine learning. We investigate a flexible means of regularization for link prediction based on…