Berlin 2018 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 16: Networks: From Topology to Dynamics (joint session DY/SOE)
SOE 16.1: Talk
Wednesday, March 14, 2018, 15:30–15:45, MA 001
Mapping and discrimination of networks in the complexity-entropy plane — •Marc Wiedermann1,2, Jonathan F. Donges1,3, Jürgen Kurths1,2, and Reik V. Donner1 — 1Potsdam Institute for Climate Impact Research — 2Humboldt University of Berlin — 3Stockholm Resilience Centre
Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category cluster in distinct areas of the resulting complexity-entropy plane. In particular, connectome networks exhibit among the highest complexity while transportation and infrastructure networks display significantly lower values. We further show that the proposed framework is useful to objectively construct threshold-based networks by choosing the threshold such that the statistical complexity is maximized.