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  • Characterization of real-world complex systems increasingly involves the study of their topological structure using graph theory. Among global network properties, small-world property, consisting in existence of relatively short paths together with high clustering of the network, is one of the most discussed and studied. When dealing with coupled dynamical systems, links among units of the system are commonly quantified by a measure of pairwise statistical dependence of observed time series (functional connectivity). We argue that the functional connectivity approach leads to upwardly biased estimates of small-world characteristics (with respect to commonly used random graph models) due to partial transitivity of the accepted functional connectivity measures such as the correlation coefficient. In particular, this may lead to observation of small-world characteristics in connectivity graphs estimated from generic randomly connected dynamical systems. The ubiquity and robustness of the phenomenon are documented by an extensive parameter study of its manifestation in a multivariate linear autoregressive process, with discussion of the potential relevance for nonlinear processes and measures.
  • Characterization of real-world complex systems increasingly involves the study of their topological structure using graph theory. Among global network properties, small-world property, consisting in existence of relatively short paths together with high clustering of the network, is one of the most discussed and studied. When dealing with coupled dynamical systems, links among units of the system are commonly quantified by a measure of pairwise statistical dependence of observed time series (functional connectivity). We argue that the functional connectivity approach leads to upwardly biased estimates of small-world characteristics (with respect to commonly used random graph models) due to partial transitivity of the accepted functional connectivity measures such as the correlation coefficient. In particular, this may lead to observation of small-world characteristics in connectivity graphs estimated from generic randomly connected dynamical systems. The ubiquity and robustness of the phenomenon are documented by an extensive parameter study of its manifestation in a multivariate linear autoregressive process, with discussion of the potential relevance for nonlinear processes and measures. (en)
Title
  • Small-world topology of functional connectivity in randomly connected dynamical systems
  • Small-world topology of functional connectivity in randomly connected dynamical systems (en)
skos:prefLabel
  • Small-world topology of functional connectivity in randomly connected dynamical systems
  • Small-world topology of functional connectivity in randomly connected dynamical systems (en)
skos:notation
  • RIV/67985807:_____/12:00380325!RIV13-GA0-67985807
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • I, P(GCP103/11/J068)
http://linked.open...iv/cisloPeriodika
  • 3
http://linked.open...vai/riv/dodaniDat
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  • 168570
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  • RIV/67985807:_____/12:00380325
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  • graph theory; nonlinear dynamical systems; small-world networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [AD3EB89A288F]
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  • Chaos
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http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 22
http://linked.open...iv/tvurceVysledku
  • Hartman, David
  • Hlinka, Jaroslav
  • Paluš, Milan
http://linked.open...ain/vavai/riv/wos
  • 000309427500007
issn
  • 1054-1500
number of pages
http://bibframe.org/vocab/doi
  • 10.1063/1.4732541
is http://linked.open...avai/riv/vysledek of
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