Literature DB >> 24653036

Control profiles of complex networks.

Justin Ruths1, Derek Ruths.   

Abstract

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.

Mesh:

Year:  2014        PMID: 24653036     DOI: 10.1126/science.1242063

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  70 in total

1.  Network Controllability in the Inferior Frontal Gyrus Relates to Controlled Language Variability and Susceptibility to TMS.

Authors:  John D Medaglia; Denise Y Harvey; Nicole White; Apoorva Kelkar; Jared Zimmerman; Danielle S Bassett; Roy H Hamilton
Journal:  J Neurosci       Date:  2018-06-08       Impact factor: 6.167

Review 2.  Brain and cognitive reserve: Translation via network control theory.

Authors:  John Dominic Medaglia; Fabio Pasqualetti; Roy H Hamilton; Sharon L Thompson-Schill; Danielle S Bassett
Journal:  Neurosci Biobehav Rev       Date:  2017-01-16       Impact factor: 8.989

3.  Structure-based control of complex networks with nonlinear dynamics.

Authors:  Jorge Gomez Tejeda Zañudo; Gang Yang; Réka Albert
Journal:  Proc Natl Acad Sci U S A       Date:  2017-06-27       Impact factor: 11.205

4.  Controllability of networked higher-dimensional systems with one-dimensional communication.

Authors:  Lin Wang; Xiaofan Wang; Guanrong Chen
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2017-03-06       Impact factor: 4.226

5.  Input graph: the hidden geometry in controlling complex networks.

Authors:  Xizhe Zhang; Tianyang Lv; Yuanyuan Pu
Journal:  Sci Rep       Date:  2016-11-30       Impact factor: 4.379

6.  Fundamental limitations of network reconstruction from temporal data.

Authors:  Marco Tulio Angulo; Jaime A Moreno; Gabor Lippner; Albert-László Barabási; Yang-Yu Liu
Journal:  J R Soc Interface       Date:  2017-02       Impact factor: 4.118

7.  Impulsive control of a nonlinear dynamical network and its application to biological networks.

Authors:  Min Luo; Jianfeng Jiao; Ruiqi Wang
Journal:  J Biol Phys       Date:  2018-10-31       Impact factor: 1.365

8.  Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree.

Authors:  Daniel Leitold; Agnes Vathy-Fogarassy; Janos Abonyi
Journal:  Sensors (Basel)       Date:  2018-09-14       Impact factor: 3.576

9.  Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

Authors:  Jason Z Kim; Jonathan M Soffer; Ari E Kahn; Jean M Vettel; Fabio Pasqualetti; Danielle S Bassett
Journal:  Nat Phys       Date:  2017-09-25       Impact factor: 20.034

10.  A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

Authors:  Wei-Feng Guo; Shao-Wu Zhang; Qian-Qian Shi; Cheng-Ming Zhang; Tao Zeng; Luonan Chen
Journal:  BMC Genomics       Date:  2018-01-19       Impact factor: 3.969

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