Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Stefan Lange*, Jonathan F. Donges, Jan Volkholz, Juergen Kurths

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Citations (Scopus)
4 Downloads (Pure)

Abstract

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge-and node-weighted graphs are discussed.

Original languageEnglish
Article number0118088
Number of pages28
JournalPloS ONE
Volume10
Issue number4
Early online date9 Apr 2015
DOIs
Publication statusPublished - 9 Apr 2015

Keywords

  • rank correlation coefficients
  • regional climate simulations
  • brain networks
  • functional connectivity
  • precipitation characteristics
  • hierarchical organization
  • South-America
  • association
  • performance
  • similarity

Cite this

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation. / Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Juergen.

In: PloS ONE, Vol. 10, No. 4, 0118088, 09.04.2015.

Research output: Contribution to journalArticle

Lange, Stefan ; Donges, Jonathan F. ; Volkholz, Jan ; Kurths, Juergen. / Local Difference Measures between Complex Networks for Dynamical System Model Evaluation. In: PloS ONE. 2015 ; Vol. 10, No. 4.
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