Nathalie Pernelle — accepted AI-conference papers

2026

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

Hugo Attali, Nathalie Pernelle, Davide Buscaldi, Fragkiskos Malliaros

IJCAI 2026

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 information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representa

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2024

Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation

Hugo Attali, Davide Buscaldi, Nathalie Pernelle

ICML 2024poster

GNNs rely on the exchange of messages to distribute information along the edges of the graph. This approach makes the efficiency of architectures highly dependent on the specific structure of the input graph. Certain graph topologies lead to inefficient information propagation, resulting in a phenom…

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