Cargando…
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...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642569/ https://www.ncbi.nlm.nih.gov/pubmed/26348923 http://dx.doi.org/10.1038/srep13786 |
_version_ | 1782400381546921984 |
---|---|
author | Kovács, István A. Mizsei, Réka Csermely, Péter |
author_facet | Kovács, István A. Mizsei, Réka Csermely, Péter |
author_sort | Kovács, István A. |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-4642569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46425692015-11-20 A unified data representation theory for network visualization, ordering and coarse-graining Kovács, István A. Mizsei, Réka Csermely, Péter Sci Rep 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/ |
spellingShingle | Article Kovács, István A. Mizsei, Réka Csermely, Péter A unified data representation theory for network visualization, ordering and coarse-graining |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642569/ https://www.ncbi.nlm.nih.gov/pubmed/26348923 http://dx.doi.org/10.1038/srep13786 |
work_keys_str_mv | AT kovacsistvana aunifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining AT mizseireka aunifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining AT csermelypeter aunifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining AT kovacsistvana unifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining AT mizseireka unifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining AT csermelypeter unifieddatarepresentationtheoryfornetworkvisualizationorderingandcoarsegraining |