3 citations · 4 across the 2 of their papers we have counts for
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stat.ML2021★ 1 cited
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…
stat.ML2020
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…
stat.ML2020★ 3 cited
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…