24 citations · 35 across the 14 of their papers we have counts for
3 papers · 1 filter
What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
Nicolas Keriven, Samuel Vaiter
We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Existing analyses relate this notion to the…
Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs
Matthieu Cordonnier, Nicolas Keriven, Nicolas Tremblay +1
We study the convergence of message passing graph neural networks on random graph models to their continuous counterpart as the number of nodes tends to infinity. Until now, this c…
Gradient scarcity with Bilevel Optimization for Graph Learning
Hashem Ghanem, Samuel Vaiter, Nicolas Keriven
A common issue in graph learning under the semi-supervised setting is referred to as gradient scarcity. That is, learning graphs by minimizing a loss on a subset of nodes causes ed…