9,599 research outputs found
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
Conedy: a scientific tool to investigate Complex Network Dynamics
We present Conedy, a performant scientific tool to numerically investigate
dynamics on complex networks. Conedy allows to create networks and provides
automatic code generation and compilation to ensure performant treatment of
arbitrary node dynamics. Conedy can be interfaced via an internal script
interpreter or via a Python module
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
Synchronization processes in complex networks
We present an extended analysis, based on the dynamics towards
synchronization of a system of coupled oscillators, of the hierarchy of
communities in complex networks. In the synchronization process, different
structures corresponding to well defined communities of nodes appear in a
hierarchical way. The analysis also provides a useful connection between
synchronization dynamics, complex networks topology and spectral graph
analysis.Comment: 16 pages, 4 figures. To appear in Physica D "Special Issue on
dynamics on complex networks
Design of Easily Synchronizable Oscillator Networks Using the Monte Carlo Optimization Method
Starting with an initial random network of oscillators with a heterogeneous
frequency distribution, its autonomous synchronization ability can be largely
improved by appropriately rewiring the links between the elements. Ensembles of
synchronization-optimized networks with different connectivities are generated
and their statistical properties are studied
Synchronization and modularity in complex networks
We investigate the connection between the dynamics of synchronization and the
modularity on complex networks. Simulating the Kuramoto's model in complex
networks we determine patterns of meta-stability and calculate the modularity
of the partition these patterns provide. The results indicate that the more
stable the patterns are, the larger tends to be the modularity of the partition
defined by them. This correlation works pretty well in homogeneous networks
(all nodes have similar connectivity) but fails when networks contain hubs,
mainly because the modularity is never improved where isolated nodes appear,
whereas in the synchronization process the characteristic of hubs is to have a
large stability when forming its own community.Comment: To appear in the Proceedings of Workshop on Complex Systems: New
Trends and Expectations, Santander, Spain, 5-9 June 200
Optimal synchronization of directed complex networks
We study optimal synchronization of networks of coupled phase oscillators. We
extend previous theory for optimizing the synchronization properties of
undirected networks to the important case of directed networks. We derive a
generalized synchrony alignment function that encodes the interplay between
network structure and the oscillators' natural frequencies and serves as an
objective measure for the network's degree of synchronization. Using the
generalized synchrony alignment function, we show that a network's
synchronization properties can be systematically optimized. This framework also
allows us to study the properties of synchrony-optimized networks, and in
particular, investigate the role of directed network properties such as nodal
in- and out-degrees. For instance, we find that in optimally rewired networks
the heterogeneity of the in-degree distribution roughly matches the
heterogeneity of the natural frequency distribution, but no such relationship
emerges for out-degrees. We also observe that a network's synchronization
properties are promoted by a strong correlation between the nodal in-degrees
and the natural frequencies of oscillators, whereas the relationship between
the nodal out-degrees and the natural frequencies has comparatively little
effect. This result is supported by our theory, which indicates that
synchronization is promoted by a strong alignment of the natural frequencies
with the left singular vectors corresponding to the largest singular values of
the Laplacian matrix
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