Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Inertia Tensor01:24

Inertia Tensor

1.1K
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
1.1K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Network Function of a Circuit01:25

Network Function of a Circuit

660
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
660
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

488
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
488

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Clinical efficacy and safety of subtotal resection of adenomyotic lesions based on the Kishi classification: a retrospective case series study.

Frontiers in medicine·2026
Same author

Weighted single-step GWAS identified candidate genes associated with semen traits in Rhode Island Red chickens.

Poultry science·2026
Same author

Optimal dismantling of directed networks.

Nature communications·2026
Same author

Development and internal validation of a clinical prediction model for postoperative urinary tract infection in older surgical patients: a retrospective cohort study.

BMC geriatrics·2026
Same author

DingkunDɑn ameliorates ovarian fibrosis and restores ovulation in a DHEA-induced PCOS rat model via inhibition of the TGF-β/Smad signaling pathway.

Tissue & cell·2026
Same author

Machine learning-based Personalized Dietary Recommendations to Achieve Desired Gut Microbial Compositions.

bioRxiv : the preprint server for biology·2026
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

A tensor-based framework for studying eigenvector multicentrality in multilayer networks.

Mincheng Wu1, Shibo He2, Yongtao Zhang1

  • 1State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China.

Proceedings of the National Academy of Sciences of the United States of America
|July 19, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a tensor-based framework for eigenvector multicentrality in multilayer networks. It quantifies interlayer influence and node importance across network layers.

Keywords:
PageRank centralityeigenvector centralitymultilayer networks

More Related Videos

Inkjet-printed Polyvinyl Alcohol Multilayers
05:11

Inkjet-printed Polyvinyl Alcohol Multilayers

Published on: May 11, 2017

13.0K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

29.2K

Related Experiment Videos

Last Updated: Jan 22, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Inkjet-printed Polyvinyl Alcohol Multilayers
05:11

Inkjet-printed Polyvinyl Alcohol Multilayers

Published on: May 11, 2017

13.0K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

29.2K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Mathematical Modeling

Background:

  • Centrality measures are crucial for understanding complex single-layer networks.
  • A unified framework for centrality in multilayer networks (multicentrality) is currently lacking.
  • Existing methods do not fully capture the intricate interplay between network layers.

Purpose of the Study:

  • To introduce a novel tensor-based framework for eigenvector multicentrality in multilayer networks.
  • To quantify the impact of interlayer influence on node centrality across different network layers.
  • To provide a systematic method for analyzing multicentrality propagation in complex systems.

Main Methods:

  • Development of a tensor-based framework to model eigenvector multicentrality.
  • Quantification of interlayer influence using adaptable functions.
  • Design of algorithms for calculating multicentrality in multilayer networks.
  • Application and validation on empirical multilayer network datasets.

Main Results:

  • The proposed framework effectively quantifies the influence of interlayer interactions on multicentrality.
  • Node multicentrality is accurately characterized, considering the multilayer structure.
  • The framework demonstrates flexibility in modeling diverse interlayer influence scenarios.
  • Empirical analysis validates the framework's ability to capture network dynamics.

Conclusions:

  • The tensor-based framework offers a robust approach to eigenvector multicentrality in multilayer networks.
  • It provides essential insights into how centrality propagates and is influenced across network layers.
  • This work lays the foundation for advanced analysis of complex systems with multiple interacting layers.