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Author: Kurths, J. × Type: Text × Subject: complex networks ×

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Now showing 1 - 3 of 3
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    General scaling of maximum degree of synchronization in noisy complex networks
    (Bristol : Institute of Physics Publishing, 2014) Traxl, D.; Boers, N.; Kurths, J.
    The effects of white noise and global coupling strength on the maximum degree of synchronization in complex networks are explored. We perform numerical simulations of generic oscillator models with both linear and non-linear coupling functions on a broad spectrum of network topologies. The oscillator models include the Fitzhugh-Nagumo model, the Izhikevich model and the Kuramoto phase oscillator model. The network topologies range from regular, random and highly modular networks to scale-free and small-world networks, with both directed and undirected edges. We then study the dependency of the maximum degree of synchronization on the global coupling strength and the noise intensity. We find a general scaling of the synchronizability, and quantify its validity by fitting a regression model to the numerical data.
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    The Dynamics of Coalition Formation on Complex Networks
    (London : Nature Publishing Group, 2015) Auer, Sören; Heitzig, J.; Kornek, U.; Schöll, E.; Kurths, J.
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    Networks from Flows - From Dynamics to Topology
    (London : Nature Publishing Group, 2014) Molkenthin, N.; Rehfeld, K.; Marwan, N.; Kurths, J.
    Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information.