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Effective Subnetwork Topology for Synchronizing Interconnected Networks of Coupled Phase Oscillators

Overview of attention for article published in Frontiers in Computational Neuroscience, March 2018
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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6 X users

Citations

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17 Dimensions

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20 Mendeley
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Title
Effective Subnetwork Topology for Synchronizing Interconnected Networks of Coupled Phase Oscillators
Published in
Frontiers in Computational Neuroscience, March 2018
DOI 10.3389/fncom.2018.00017
Pubmed ID
Authors

Hideaki Yamamoto, Shigeru Kubota, Fabio A. Shimizu, Ayumi Hirano-Iwata, Michio Niwano

Abstract

A system consisting of interconnected networks, or a network of networks (NoN), appears diversely in many real-world systems, including the brain. In this study, we consider NoNs consisting of heterogeneous phase oscillators and investigate how the topology of subnetworks affects the global synchrony of the network. The degree of synchrony and the effect of subnetwork topology are evaluated based on the Kuramoto order parameter and the minimum coupling strength necessary for the order parameter to exceed a threshold value, respectively. In contrast to an isolated network in which random connectivity is favorable for achieving synchrony, NoNs synchronize with weaker interconnections when the degree distribution of subnetworks is heterogeneous, suggesting the major role of the high-degree nodes. We also investigate a case in which subnetworks with different average natural frequencies are coupled to show that direct coupling of subnetworks with the largest variation is effective for synchronizing the whole system. In real-world NoNs like the brain, the balance of synchrony and asynchrony is critical for its function at various spatial resolutions. Our work provides novel insights into the topological basis of coordinated dynamics in such networks.

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X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Student > Ph. D. Student 5 25%
Lecturer 1 5%
Student > Doctoral Student 1 5%
Other 1 5%
Other 3 15%
Unknown 3 15%
Readers by discipline Count As %
Engineering 5 25%
Neuroscience 4 20%
Agricultural and Biological Sciences 1 5%
Computer Science 1 5%
Mathematics 1 5%
Other 4 20%
Unknown 4 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 April 2023.
All research outputs
#7,786,699
of 23,650,645 outputs
Outputs from Frontiers in Computational Neuroscience
#425
of 1,378 outputs
Outputs of similar age
#133,220
of 331,111 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#12
of 25 outputs
Altmetric has tracked 23,650,645 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,378 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,111 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.