1 citations · 1 across the 4 of their papers we have counts for
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A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
Nicolas Tremblay, Benjamin Ricaud, Filippo Maria Bianchi
We propose the Tikhonov layer, a graph neural network layer that is interpretable by design: once trained, its learned parameters directly reveal which node features and which aspe…
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…
Optimal Laplacian regularization for sparse spectral community detection
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferabl…
Approximating Spectral Clustering via Sampling: a Review
Nicolas Tremblay, Andreas Loukas
Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. T…
Graph sampling with determinantal processes
Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, ie, in cases where the spectr…