7 citations · 7 across the 4 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…
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…
Compressive K-means
Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin +1
The Lloyd-Max algorithm is a classical approach to perform K-means clustering. Unfortunately, its cost becomes prohibitive as the training dataset grows large. We propose a compres…