most citedGraph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Fast and Accurate Quotation Attribution in Literary Texts

Gaspard Michel, Hugo Attali, Elena V. Epure

Attributing quotations to their speakers in literary texts remains an open challenge. Standard methods, which independently predict a speaker mention for each quotation, are effici…

cs.LG2026

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,…

cs.LG2026

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…

cs.LG20262 cited

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…

cs.LG2025

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…