1 citations · 1 across the 2 of their papers we have counts for
11 papers
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…
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
This article considers the problem of community detection in sparse dynamical graphs in which the community structure evolves over time. A fast spectral algorithm based on an exten…
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…
Smoothing graph signals via random spanning forests
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé +1
Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo…
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…
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the…