Non-parametric resampling of random walks for spectral network clustering
Physics and Society
2014-02-25 v2 Social and Information Networks
Applications
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.
Cite
@article{arxiv.1304.4156,
title = {Non-parametric resampling of random walks for spectral network clustering},
author = {Fabrizio De Vico Fallani and Vincenzo Nicosia and Vito Latora and Mario Chavez},
journal= {arXiv preprint arXiv:1304.4156},
year = {2014}
}
Comments
5 pages, 2 figures