A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we sh...

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Main Authors:	Kovács, István A., Mizsei, Réka, Csermely, Péter
Format:	Online
Language:	English
Published:	Nature Publishing Group 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642569/

id	pubmed-4642569
recordtype	oai_dc
spelling	pubmed-46425692015-11-20 A unified data representation theory for network visualization, ordering and coarse-graining Kovács, István A. Mizsei, Réka Csermely, Péter Article Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form. Nature Publishing Group 2015-09-08 /pmc/articles/PMC4642569/ /pubmed/26348923 http://dx.doi.org/10.1038/srep13786 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kovács, István A. Mizsei, Réka Csermely, Péter
spellingShingle	Kovács, István A. Mizsei, Réka Csermely, Péter A unified data representation theory for network visualization, ordering and coarse-graining
author_facet	Kovács, István A. Mizsei, Réka Csermely, Péter
author_sort	Kovács, István A.
title	A unified data representation theory for network visualization, ordering and coarse-graining
title_short	A unified data representation theory for network visualization, ordering and coarse-graining
title_full	A unified data representation theory for network visualization, ordering and coarse-graining
title_fullStr	A unified data representation theory for network visualization, ordering and coarse-graining
title_full_unstemmed	A unified data representation theory for network visualization, ordering and coarse-graining
title_sort	unified data representation theory for network visualization, ordering and coarse-graining
description	Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.
publisher	Nature Publishing Group
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642569/
_version_	1613500368476438528

A unified data representation theory for network visualization, ordering and coarse-graining

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