3 citations · 4 across the 2 of their papers we have counts for
9 papers
On the Universality of Graph Neural Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs. In the large graph limit, GNNs are known to converge to certain "continuous" mode…
Fast Graph Kernel with Optical Random Features
Hashem Ghanem, Nicolas Keriven, Nicolas Tremblay
The graphlet kernel is a classical method in graph classification. It however suffers from a high computation cost due to the isomorphism test it includes. As a generic proxy, and…
Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
Nicolas Keriven, Alberto Bietti, Samuel Vaiter
We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs, where nodes are represented by random latent variables a…
Sparse and Smooth: improved guarantees for Spectral Clustering in the Dynamic Stochastic Block Model
Nicolas Keriven, Samuel Vaiter
In this paper, we analyse classical variants of the Spectral Clustering (SC) algorithm in the Dynamic Stochastic Block Model (DSBM). Existing results show that, in the relatively s…
Universal Invariant and Equivariant Graph Neural Networks
Nicolas Keriven, Gabriel Peyré
Graph Neural Networks (GNN) come in many flavors, but should always be either invariant (permutation of the nodes of the input graph does not affect the output) or equivariant (per…
Support Localization and the Fisher Metric for off-the-grid Sparse Regularization
Clarice Poon, Nicolas Keriven, Gabriel Peyré
Sparse regularization is a central technique for both machine learning (to achieve supervised features selection or unsupervised mixture learning) and imaging sciences (to achieve…