Structure-based control of complex networks with nonlinear dynamics

Jorge Gomez Tejeda Zañudo, Gang Yang, Réka Albert, Herbert Levine

Research output: Contribution to journalArticle

59 Scopus citations

Abstract

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Original languageEnglish (US)
Pages (from-to)7234-7239
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number28
DOIs
StatePublished - Jul 11 2017

All Science Journal Classification (ASJC) codes

  • General

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