Evidence map›Paper›PMID 32782263›Full record

ArticleNature communications2020

Synchronization of complex human networks.

Shir Shahal, Ateret Wurzberg, Inbar Sibony, Hamootal Duadi, Elad Shniderman, Daniel Weymouth, Nir Davidson, Moti Fridman

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  6. Review
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  12. Synchronization in simplicial complexes of memristive Rulkov neurons.Frontiers in computational neuroscience · 2023
    Article
  13. Article
  14. Article
  15. Complex Networks and theEntropy (Basel, Switzerland) · 2022
    Article
  16. Article
  17. A metastable attractor model of self-other integration (MEAMSO) in rhythmic synchronization.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Article
  18. Article
  19. Article
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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

8 authors.

Shir ShahalFaculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, 5290002, Ramat Gan, Israel.
Ateret WurzbergFaculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, 5290002, Ramat Gan, Israel.
Inbar SibonyFaculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, 5290002, Ramat Gan, Israel.
Hamootal DuadiFaculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, 5290002, Ramat Gan, Israel.
Elad ShnidermanDepartment of Music, Stony Brook University, Stony Brook, NY, 11794, USA.
Daniel WeymouthDepartment of Music, Stony Brook University, Stony Brook, NY, 11794, USA.
Nir DavidsonDepartment of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel.ORCID http://orcid.org/0000-0001-7526-851X
Moti FridmanFaculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, 5290002, Ramat Gan, Israel. mordechai.fridman@biu.ac.il.ORCID http://orcid.org/0000-0002-8883-9964

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The synchronization of human networks is essential for our civilization and understanding its dynamics is important to many aspects of our lives. Human ensembles were investigated, but in noisy environments and with limited control over the network parameters which govern the network dynamics. Specifically, research has focused predominantly on all-to-all coupling, whereas current social networks and human interactions are often based on complex coupling configurations. Here, we study the synchronization between violin players in complex networks with full and accurate control over the network connectivity, coupling strength, and delay. We show that the players can tune their playing period and delete connections by ignoring frustrating signals, to find a stable solution. These additional degrees of freedom enable new strategies and yield better solutions than are possible within current models such as the Kuramoto model. Our results may influence numerous fields, including traffic management, epidemic control, and stock market dynamics.

Indexed as

Interpersonal RelationsModels, PsychologicalSocial BehaviorSocial NetworkingFemaleFrustrationHumansMaleNonlinear Dynamics

Identifiers

PMID32782263
PMCPMC7419301

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.