Network evolution towards optimal dynamical performance
Steffen Karalus,Markus Porto +1 more
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TL;DR: In this paper, a generic approach to investigate the mutual interdependence between the behavior of dynamical processes on networks and the underlying topologies is presented, which is applicable to a wide class of dynamics.
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Abstract: Understanding the mutual interdependence between the behavior of dynamical processes on networks and the underlying topologies promises new insight for a large class of empirical networks. We present a generic approach to investigate this relationship which is applicable to a wide class of dynamics, namely to evolve networks using a performance measure based on the whole spectrum of the dynamics' time evolution operator. As an example, we consider the graph Laplacian describing diffusion processes, and we evolve the network structure such that a given sub-diffusive behavior emerges.
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Citations
The effect of interdependence on the percolation of interdependent networks
TL;DR: Two stochastic models are proposed to generate a system composed of two interdependent scale-free or Erdős–Renyi networks where interdependent nodes are connected with an exponential or power-law relation, as well as different dependence strength, respectively.
13
Symmetry-based coarse-graining of evolved dynamical networks
Steffen Karalus,Joachim Krug +1 more
TL;DR: The resulting coarse-grained networks provide an intuitive view of how the anomalous diffusive properties can be realized in the evolved structures.
5
Symmetry-based coarse-graining of evolved dynamical networks
Steffen Karalus,Joachim Krug +1 more
TL;DR: In this article, the underlying backbone structures and how they contribute to the spectrum of the graph Laplacian were investigated. And the resulting coarse-grained networks provide an intuitive view of how the anomalous diffusive properties can be realized in the evolved structures.
3
Reconstruction of evolved dynamic networks from degree correlations.
Steffen Karalus,Joachim Krug +1 more
TL;DR: It turns out that the degree distribution alone is not sufficient to generate the spectral scaling and the degree-dependent clustering has only an indirect influence, so the two-point correlations are found to be the dominant characteristic for the power-law scaling over a broader eigenvalue range.
2
•Posted Content
Evolutionary design of non-frustrated networks of phase-repulsive oscillators
TL;DR: In this article, an evolutionary optimisation algorithm is employed to design networks of phase-repulsive oscillators that achieve an anti-phase synchronised state by introducing the link frustration, the evolutionary process is implemented by rewiring the links with probability proportional to their frustration, until the final network displaying a unique non-frustrated dynamical state is reached.
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