Extracting labeled topological patterns from samples of networks.

An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investig...

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Autores principales:	Christoph Schmidt, Thomas Weiss, Thomas Lehmann, Herbert Witte, Lutz Leistritz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1e35863305a6433a9e53cacc26d48431
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spelling	oai:doaj.org-article:1e35863305a6433a9e53cacc26d484312021-11-18T09:00:19ZExtracting labeled topological patterns from samples of networks.1932-620310.1371/journal.pone.0070497https://doaj.org/article/1e35863305a6433a9e53cacc26d484312013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23950945/?tool=EBIhttps://doaj.org/toc/1932-6203An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.Christoph SchmidtThomas WeissThomas LehmannHerbert WitteLutz LeistritzPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e70497 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Christoph Schmidt Thomas Weiss Thomas Lehmann Herbert Witte Lutz Leistritz Extracting labeled topological patterns from samples of networks.
description	An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.
format	article
author	Christoph Schmidt Thomas Weiss Thomas Lehmann Herbert Witte Lutz Leistritz
author_facet	Christoph Schmidt Thomas Weiss Thomas Lehmann Herbert Witte Lutz Leistritz
author_sort	Christoph Schmidt
title	Extracting labeled topological patterns from samples of networks.
title_short	Extracting labeled topological patterns from samples of networks.
title_full	Extracting labeled topological patterns from samples of networks.
title_fullStr	Extracting labeled topological patterns from samples of networks.
title_full_unstemmed	Extracting labeled topological patterns from samples of networks.
title_sort	extracting labeled topological patterns from samples of networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/1e35863305a6433a9e53cacc26d48431
work_keys_str_mv	AT christophschmidt extractinglabeledtopologicalpatternsfromsamplesofnetworks AT thomasweiss extractinglabeledtopologicalpatternsfromsamplesofnetworks AT thomaslehmann extractinglabeledtopologicalpatternsfromsamplesofnetworks AT herbertwitte extractinglabeledtopologicalpatternsfromsamplesofnetworks AT lutzleistritz extractinglabeledtopologicalpatternsfromsamplesofnetworks
_version_	1718421010579980288

Extracting labeled topological patterns from samples of networks.

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