83 citations · 332 across the 27 of their papers we have counts for
5 papers · 1 filter
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…
Estimating the inverse trace using random forests on graphs
Simon Barthelmé, Nicolas Tremblay, Alexandre Gaudillière +2
Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are un…
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…