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Control Profiles of Complex Networks
by
Ruths, Justin
, Ruths, Derek
in
Algorithms
/ Brain - physiology
/ Cactus
/ Complex networks
/ Complex systems
/ Computer Simulation
/ Conservation laws
/ Control
/ Design engineering
/ Disease models
/ Engineers
/ Humans
/ Information Transfer
/ Linear Models
/ Mathematical minima
/ mathematical models
/ mathematical theory
/ Models, Theoretical
/ Nerve Net
/ Networks
/ Social influence
/ Social Support
/ Systems Analysis
/ Systems Theory
/ Topology
/ traffic
/ Traffic accidents & safety
/ transcription (genetics)
/ Transportation
2014
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Control Profiles of Complex Networks
by
Ruths, Justin
, Ruths, Derek
in
Algorithms
/ Brain - physiology
/ Cactus
/ Complex networks
/ Complex systems
/ Computer Simulation
/ Conservation laws
/ Control
/ Design engineering
/ Disease models
/ Engineers
/ Humans
/ Information Transfer
/ Linear Models
/ Mathematical minima
/ mathematical models
/ mathematical theory
/ Models, Theoretical
/ Nerve Net
/ Networks
/ Social influence
/ Social Support
/ Systems Analysis
/ Systems Theory
/ Topology
/ traffic
/ Traffic accidents & safety
/ transcription (genetics)
/ Transportation
2014
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Do you wish to request the book?
Control Profiles of Complex Networks
by
Ruths, Justin
, Ruths, Derek
in
Algorithms
/ Brain - physiology
/ Cactus
/ Complex networks
/ Complex systems
/ Computer Simulation
/ Conservation laws
/ Control
/ Design engineering
/ Disease models
/ Engineers
/ Humans
/ Information Transfer
/ Linear Models
/ Mathematical minima
/ mathematical models
/ mathematical theory
/ Models, Theoretical
/ Nerve Net
/ Networks
/ Social influence
/ Social Support
/ Systems Analysis
/ Systems Theory
/ Topology
/ traffic
/ Traffic accidents & safety
/ transcription (genetics)
/ Transportation
2014
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Journal Article
Control Profiles of Complex Networks
2014
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Overview
Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.
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