4 papers
Spectrum Estimation through Kirchhoff Random Forests
Simon Barthelmé, Fabienne Castell, Alexandre Gaudillière +3
Given a non-oriented edge-weighted graph, we show how to make some estimation of the associated Laplacian eigenvalues through Monte Carlo evaluation of spectral quantities computed…
Estimating a graph's spectrum via random Kirchhoff forests
Simon Barthelmé, Fabienne Castell, Alexandre Gaudillière +3
Exact eigendecomposition of large matrices is very expensive, and it is practically impossible to compute exact eigenvalues. Instead, one may set a more modest goal of approaching…
Node Regression on Latent Position Random Graphs via Local Averaging
Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé +1
Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators…
Random Multi-Type Spanning Forests for Synchronization on Sparse Graphs
Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelmé +1
Random diffusions are a popular tool in Monte-Carlo estimations, with well established algorithms such as Walk-on-Spheres (WoS) going back several decades. In this work, we introdu…