title: Nonparametric resampling of random walks for spectral network clustering.
abstract: Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.
authors.affiliation: CNRS UMR-7225, Hôpital de la Pitié-Salpêtrière, Paris, France.
authors.forename: Fabrizio
authors.initials: F
authors.lastname: De Vico Fallani
authors.affiliation: School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E1 4NS, London, United Kingdom.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Nicosia
authors.affiliation: School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E1 4NS, London, United Kingdom and Dipartimento di Fisica e Astronomia, Universitá di Catania, Via S. Sofia 61, 95123, Catania, Italy.
authors.forename: Vito
authors.initials: V
authors.lastname: Latora
authors.affiliation: CNRS UMR-7225, Hôpital de la Pitié-Salpêtrière, Paris, France.
authors.forename: Mario
authors.initials: M
authors.lastname: Chavez
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical