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  Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

Wiedermann, M., Donges, J. F., Kurths, J., Donner, R. V. (2016): Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes. - Physical Review E, 93, 42308.
https://doi.org/10.1103/PhysRevE.93.042308

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 Creators:
Wiedermann, Marc1, Author              
Donges, Jonathan Friedemann1, Author              
Kurths, Jürgen1, Author              
Donner, Reik V.1, Author              
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1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

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 Dates: 2016
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevE.93.042308
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7213
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Organisational keyword: RD4 - Complexity Science
Organisational keyword: RD1 - Earth System Analysis
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: FutureLab - Game Theory & Networks of Interacting Agents
Working Group: Whole Earth System Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Physical Review E
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 93 Sequence Number: 42308 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150218