Using bootstrap for statistical inference on random graphs
Abstract
In this paper, we propose new nonparametric approach to network inference that may be viewed as a fusion of block sampling procedures for temporally and spatially dependent processes with the classical network methodology. We develop estimation and uncertainty quantification procedures for network mean degree using a "patchwork" sample and nonparametric bootstrap, under the assumption of unknown degree distribution. We investigate asymptotic properties of the proposed patchwork bootstrap procedure and present cross-validation methodology for selecting an optimal patch size. We validate the new patchwork bootstrap on simulated networks with short and long tailed mean degree distributions, and revisit the Erdos collaboration data to illustrate the proposed methodology.
Cite
@article{arxiv.1402.3647,
title = {Using bootstrap for statistical inference on random graphs},
author = {Mary E. Thompson and Lilia Leticia Ramirez Ramirez and Vyacheslav Lyubchich and Yulia R. Gel},
journal= {arXiv preprint arXiv:1402.3647},
year = {2016}
}
Comments
The paper has been withdrawn by the authors: a general revision of methodology is needed