Small-world bias of correlation networks: from brain to climate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F17%3A43915451" target="_blank" >RIV/00023752:_____/17:43915451 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/17:00473721 RIV/00023001:_____/17:00075930 RIV/00216208:11320/17:10361960

Výsledek na webu

<a href="http://aip.scitation.org/doi/10.1063/1.4977951" target="_blank" >http://aip.scitation.org/doi/10.1063/1.4977951</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4977951" target="_blank" >10.1063/1.4977951</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Small-world bias of correlation networks: from brain to climate

Popis výsledku v původním jazyce

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948 – 2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared to a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, a comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.

Název v anglickém jazyce

Small-world bias of correlation networks: from brain to climate

Popis výsledku anglicky

Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948 – 2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared to a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, a comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chaos

ISSN

1054-1500

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

31

Strana od-do

"Article Number: 035812"

Kód UT WoS článku

000400899300006

EID výsledku v databázi Scopus

2-s2.0-85015619793