Higher-Order Spectral Clustering for Geometric Graphs - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Fourier Analysis and Applications Year : 2021

Higher-Order Spectral Clustering for Geometric Graphs

Abstract

The present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering. It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector associated with a higher-order eigenvalue. We establish the weak consistency of this algorithm for a wide class of geometric graphs which we call Soft Geometric Block Model. A small adjustment of the algorithm provides strong consistency. We also show that our method is effective in numerical experiments even for graphs of modest size.
Fichier principal
Vignette du fichier
Avrachenkov2021_Article_Higher-OrderSpectralClustering.pdf (834.68 Ko) Télécharger le fichier
Origin : Publication funded by an institution

Dates and versions

hal-03169834 , version 1 (15-03-2021)

Identifiers

Cite

Konstantin Avrachenkov, Andrei Bobu, Maximilien Dreveton. Higher-Order Spectral Clustering for Geometric Graphs. Journal of Fourier Analysis and Applications, 2021, 27, ⟨10.1007/s00041-021-09825-2⟩. ⟨hal-03169834⟩
92 View
122 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More