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

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/persons/resource/Marc.Wiedermann

Wiedermann,  Marc
Potsdam Institute for Climate Impact Research;

/persons/resource/Donges

Donges,  Jonathan Friedemann
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

/persons/resource/Reik.Donner

Donner,  Reik V.
Potsdam Institute for Climate Impact Research;

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Zitation

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


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_20951
Zusammenfassung
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.