2 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
Local Evidence and Geometric Readout Repair in Trained GNNs
Nadi Tomeh, Hugo Attali
Many node-classification GNNs apply a linear classifier to a nonnegative mixture of local messages. An error can reflect either poor mixture weights or a reachable logit set poorly…
Ramanujan Graph Rewiring with Non Negative Resistance Curvature
Hugo Attali, Rachid El Jouhri
Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating information across edges. However,…
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
Hugo Attali, Nathalie Pernelle, Davide Buscaldi +1
Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where inform…
Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
Hugo Attali, Thomas Papastergiou, Nathalie Pernelle +1
Graph Neural Networks (GNNs) have emerged as the leading paradigm for learning over graph-structured data. However, their performance is limited by issues inherent to graph topolog…
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
Hugo Attali, Davide Buscaldi, Nathalie Pernelle +1
Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where inform…