Bootstrap quantification of estimation uncertainties in network degree distributions

Abstract We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the “block...

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Autores principales:	Yulia R. Gel, Vyacheslav Lyubchich, L. Leticia Ramirez Ramirez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fbaa778d226d40e5bc096a82d7dd2355
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spelling	oai:doaj.org-article:fbaa778d226d40e5bc096a82d7dd23552021-12-02T11:52:29ZBootstrap quantification of estimation uncertainties in network degree distributions10.1038/s41598-017-05885-x2045-2322https://doaj.org/article/fbaa778d226d40e5bc096a82d7dd23552017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05885-xhttps://doaj.org/toc/2045-2322Abstract We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the “blocking” argument, developed for bootstrapping of time series and re-tiling of spatial data, to random networks. We first sample a set of multiple ego networks of varying orders that form a patch, or a network block analogue, and then resample the data within patches. To select an optimal patch size, we develop a new computationally efficient and data-driven cross-validation algorithm. The proposed fast patchwork bootstrap (FPB) methodology further extends the ideas for a case of network mean degree, to inference on a degree distribution. In addition, the FPB is substantially less computationally expensive, requires less information on a graph, and is free from nuisance parameters. In our simulation study, we show that the new bootstrap method outperforms competing approaches by providing sharper and better-calibrated confidence intervals for functions of a network degree distribution than other available approaches, including the cases of networks in an ultra sparse regime. We illustrate the FPB in application to collaboration networks in statistics and computer science and to Wikipedia networks.Yulia R. GelVyacheslav LyubchichL. Leticia Ramirez RamirezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yulia R. Gel Vyacheslav Lyubchich L. Leticia Ramirez Ramirez Bootstrap quantification of estimation uncertainties in network degree distributions
description	Abstract We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are assumed to be unknown. The key idea is based on adaptation of the “blocking” argument, developed for bootstrapping of time series and re-tiling of spatial data, to random networks. We first sample a set of multiple ego networks of varying orders that form a patch, or a network block analogue, and then resample the data within patches. To select an optimal patch size, we develop a new computationally efficient and data-driven cross-validation algorithm. The proposed fast patchwork bootstrap (FPB) methodology further extends the ideas for a case of network mean degree, to inference on a degree distribution. In addition, the FPB is substantially less computationally expensive, requires less information on a graph, and is free from nuisance parameters. In our simulation study, we show that the new bootstrap method outperforms competing approaches by providing sharper and better-calibrated confidence intervals for functions of a network degree distribution than other available approaches, including the cases of networks in an ultra sparse regime. We illustrate the FPB in application to collaboration networks in statistics and computer science and to Wikipedia networks.
format	article
author	Yulia R. Gel Vyacheslav Lyubchich L. Leticia Ramirez Ramirez
author_facet	Yulia R. Gel Vyacheslav Lyubchich L. Leticia Ramirez Ramirez
author_sort	Yulia R. Gel
title	Bootstrap quantification of estimation uncertainties in network degree distributions
title_short	Bootstrap quantification of estimation uncertainties in network degree distributions
title_full	Bootstrap quantification of estimation uncertainties in network degree distributions
title_fullStr	Bootstrap quantification of estimation uncertainties in network degree distributions
title_full_unstemmed	Bootstrap quantification of estimation uncertainties in network degree distributions
title_sort	bootstrap quantification of estimation uncertainties in network degree distributions
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/fbaa778d226d40e5bc096a82d7dd2355
work_keys_str_mv	AT yuliargel bootstrapquantificationofestimationuncertaintiesinnetworkdegreedistributions AT vyacheslavlyubchich bootstrapquantificationofestimationuncertaintiesinnetworkdegreedistributions AT lleticiaramirezramirez bootstrapquantificationofestimationuncertaintiesinnetworkdegreedistributions
_version_	1718395082984390656

Bootstrap quantification of estimation uncertainties in network degree distributions

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