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Nonparametric resampling of random walks for spectral network clustering.

Fabrizio de Vico Fallani 1 Vincenzo Nicosia 2 Vito Latora 2 Mario Chavez 1
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
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.
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Contributor : Fabrizio de Vico Fallani <>
Submitted on : Monday, May 19, 2014 - 2:48:38 PM
Last modification on : Thursday, August 20, 2020 - 3:05:05 AM

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Fabrizio de Vico Fallani, Vincenzo Nicosia, Vito Latora, Mario Chavez. Nonparametric resampling of random walks for spectral network clustering.. Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, American Physical Society, 2014, 89 (1), pp.012802. ⟨10.1103/PhysRevE.89.012802⟩. ⟨hal-00992974⟩



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