MARC Record from Deutsche Nationalbibliothek

LEADER: 05163nam a2200601uc 4500
001 1134867786
003 DE-101
005 20180212093114.0
007 cr||||||||||||
008 170608s2012 gw |||||o|||| 00||||eng
015 $a17,O07$2dnb
016 7 $2DE-101$a1134867786
024 7 $a10.1371/journal.pone.0037911$2doi
024 7 $2urn$aurn:nbn:de:bsz:25-freidok-118718
035 $a(DE-599)DNB1134867786
035 $a(OCoLC)992999429
040 $a1240$bger$cDE-101$d1247$erda
041 $aeng
044 $cXA-DE-BW
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100 1 $0(DE-588)136073042$0https://d-nb.info/gnd/136073042$0(DE-101)136073042$aCardanobile, Stefano$d1980-$eVerfasser$4aut$2gnd
245 10 $aInferring general relations between network characteristics from specific network ensembles
264 1 $aFreiburg$bUniversität$c2012
300 $aOnline-Ressource
336 $aText$btxt$2rdacontent
337 $aComputermedien$bc$2rdamedia
338 $aOnline-Ressource$bcr$2rdacarrier
500 $aPLoS ONE. 7, 6 (2012), e37911, DOI 10.1371/journal.pone.0037911, issn: 1932-6203
500 $aIN COPYRIGHT http://rightsstatements.org/page/InC/1.0 rs
520 $aAbstract: Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget’s Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models
583 1 $aArchivierung/Langzeitarchivierung gewährleistet$5DE-101$2pdager
650 7 $0(DE-588)4075298-7$0https://d-nb.info/gnd/4075298-7$0(DE-101)040752984$aNetzwerkanalyse$2gnd
650 7 $0(DE-588)4226127-2$0https://d-nb.info/gnd/4226127-2$0(DE-101)042261279$aNeuronales Netz$2gnd
650 7 $0(DE-588)4014894-4$0https://d-nb.info/gnd/4014894-4$0(DE-101)040148947$aEntropie$2gnd
650 7 $0(DE-588)4113782-6$0https://d-nb.info/gnd/4113782-6$0(DE-101)041137825$aGraphentheorie$2gnd
650 7 $0(DE-588)4013396-5$0https://d-nb.info/gnd/4013396-5$0(DE-101)040133966$aDynamisches System$2gnd
653 $aEigenvalues
653 $aLinear regression analysis
653 $aMetabolic networks
653 $a(local)article
700 1 $0(DE-588)1128412462$0https://d-nb.info/gnd/1128412462$0(DE-101)1128412462$aPernice, Volker$eVerfasser$4aut$2gnd
700 1 $0(DE-588)1131881753$0https://d-nb.info/gnd/1131881753$0(DE-101)1131881753$aDeger, Moritz$eVerfasser$4aut$2gnd
700 1 $0(DE-588)1101391669$0https://d-nb.info/gnd/1101391669$0(DE-101)1101391669$aRotter, Stefan$eVerfasser$4aut$2gnd
710 2 $0(DE-588)1064661718$0https://d-nb.info/gnd/1064661718$0(DE-101)1064661718$aAlbert-Ludwigs-Universität Freiburg$bBernstein Center Freiburg$eMitwirkender$4ctb
710 2 $0(DE-588)2039944-3$0https://d-nb.info/gnd/2039944-3$0(DE-101)004948726$aAlbert-Ludwigs-Universität Freiburg$bFakultät für Biologie$eMitwirkender$4ctb
710 2 $0(DE-588)2024338-8$0https://d-nb.info/gnd/2024338-8$0(DE-101)004816633$aAlbert-Ludwigs-Universität Freiburg$eVerlag$4pbl
850 $aDE-101a$aDE-101b
856 40 $uhttps://doi.org/10.1371/journal.pone.0037911$xResolving-System
856 40 $uhttps://nbn-resolving.org/urn:nbn:de:bsz:25-freidok-118718$xResolving-System
856 0 $uhttps://d-nb.info/1134867786/34$xLangzeitarchivierung Nationalbibliothek
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883 0 $81\p$amaschinell gebildet$c0,978$d20170609$qDE-101
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