Extreme fluctuations in noisy task-completion landscapes on scale-free networks
We study the statistics and scaling of extreme fluctuations in noisy task-completion landscapes, such as those emerging in synchronized distributed-computing networks, or generic causally constrained ...
We study the statistics and scaling of extreme fluctuations in noisy task-completion landscapes, such as those emerging in synchronized distributed-computing networks, or generic causally constrained ...
Nature-inspired interconnects for self-assembled large-scale network-on-chip designs
Future nanoscale electronics built up from an Avogadro number of components need efficient, highly scalable, and robust means of communication in order to be competitive with traditional silicon appro...
Future nanoscale electronics built up from an Avogadro number of components need efficient, highly scalable, and robust means of communication in order to be competitive with traditional silicon appro...
Optimization in gradient networks
Chaos 17, 026105 (2007); doi:10.1063/1.2737825
Published 28 June 2007
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Gradient networks can be used to model the dominant structure of complex networks. Previous work has focused on random gradient networks. Here we study gradient networks that minimize jamming on substrate networks with scale-free and Erd
s-Rényi structure. We introduce structural correlations and strongly reduce congestion occurring on the network by using a Monte Carlo optimization scheme. This optimization alters the degree distribution and other structural properties of the resulting gradient networks. These results are expected to be relevant for transport and other dynamical processes in real network systems.
©2007 American Institute of Physics
| History: | Received 8 January 2007; accepted 13 April 2007; published 28 June 2007 |
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http://link.aip.org/link/?CHAOEH/17/026105/1 |
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REFERENCES (11)
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