Evidence map›Paper›PMID 28655847›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2017

Structure-based control of complex networks with nonlinear dynamics.

Jorge Gomez Tejeda Zañudo, Gang Yang, Réka Albert

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

100 citing papers in PubMed.

  1. Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  4. A complex network perspective on brain disease.Biological reviews of the Cambridge Philosophical Society · 2026
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  17. Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024
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40 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jorge Gomez Tejeda ZañudoDepartment of Physics, The Pennsylvania State University, University Park, PA 16802-6300; jgtz@phys.psu.edu.
Gang YangDepartment of Physics, The Pennsylvania State University, University Park, PA 16802-6300.
Réka AlbertDepartment of Physics, The Pennsylvania State University, University Park, PA 16802-6300.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

biological networkscomplex networksnetwork controlnonlinear dynamics

Identifiers

PMID28655847
PMCPMC5514702

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.