Nonparametric sparsification of complex multiscale networks.

Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualizat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nicholas J Foti, James M Hughes, Daniel N Rockmore
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8efb86777ac0431dafe954bbee12ca55
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8efb86777ac0431dafe954bbee12ca55
record_format	dspace
spelling	oai:doaj.org-article:8efb86777ac0431dafe954bbee12ca552021-11-18T06:59:09ZNonparametric sparsification of complex multiscale networks.1932-620310.1371/journal.pone.0016431https://doaj.org/article/8efb86777ac0431dafe954bbee12ca552011-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21346815/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualization and analysis of such networks, especially when the number of nodes is large, can pose significant challenges which are often met by reducing the edge set. Any effective "sparsification" must retain and reflect the important structure in the network. A common method is to simply apply a hard threshold, keeping only those edges whose weight exceeds some predetermined value. A more principled approach is to extract the multiscale "backbone" of a network by retaining statistically significant edges through hypothesis testing on a specific null model, or by appropriately transforming the original weight matrix before applying some sort of threshold. Unfortunately, approaches such as these can fail to capture multiscale structure in which there can be small but locally statistically significant similarity between nodes. In this paper, we introduce a new method for backbone extraction that does not rely on any particular null model, but instead uses the empirical distribution of similarity weight to determine and then retain statistically significant edges. We show that our method adapts to the heterogeneity of local edge weight distributions in several paradigmatic real world networks, and in doing so retains their multiscale structure with relatively insignificant additional computational costs. We anticipate that this simple approach will be of great use in the analysis of massive, highly connected weighted networks.Nicholas J FotiJames M HughesDaniel N RockmorePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 2, p e16431 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicholas J Foti James M Hughes Daniel N Rockmore Nonparametric sparsification of complex multiscale networks.
description	Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualization and analysis of such networks, especially when the number of nodes is large, can pose significant challenges which are often met by reducing the edge set. Any effective "sparsification" must retain and reflect the important structure in the network. A common method is to simply apply a hard threshold, keeping only those edges whose weight exceeds some predetermined value. A more principled approach is to extract the multiscale "backbone" of a network by retaining statistically significant edges through hypothesis testing on a specific null model, or by appropriately transforming the original weight matrix before applying some sort of threshold. Unfortunately, approaches such as these can fail to capture multiscale structure in which there can be small but locally statistically significant similarity between nodes. In this paper, we introduce a new method for backbone extraction that does not rely on any particular null model, but instead uses the empirical distribution of similarity weight to determine and then retain statistically significant edges. We show that our method adapts to the heterogeneity of local edge weight distributions in several paradigmatic real world networks, and in doing so retains their multiscale structure with relatively insignificant additional computational costs. We anticipate that this simple approach will be of great use in the analysis of massive, highly connected weighted networks.
format	article
author	Nicholas J Foti James M Hughes Daniel N Rockmore
author_facet	Nicholas J Foti James M Hughes Daniel N Rockmore
author_sort	Nicholas J Foti
title	Nonparametric sparsification of complex multiscale networks.
title_short	Nonparametric sparsification of complex multiscale networks.
title_full	Nonparametric sparsification of complex multiscale networks.
title_fullStr	Nonparametric sparsification of complex multiscale networks.
title_full_unstemmed	Nonparametric sparsification of complex multiscale networks.
title_sort	nonparametric sparsification of complex multiscale networks.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/8efb86777ac0431dafe954bbee12ca55
work_keys_str_mv	AT nicholasjfoti nonparametricsparsificationofcomplexmultiscalenetworks AT jamesmhughes nonparametricsparsificationofcomplexmultiscalenetworks AT danielnrockmore nonparametricsparsificationofcomplexmultiscalenetworks
_version_	1718424118203777024

Nonparametric sparsification of complex multiscale networks.

Ejemplares similares