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

Trait Centrality01:21

Trait Centrality

75
Trait centrality refers to the degree to which a particular characteristic influences the overall impression of an individual. Some traits exert a disproportionately strong impact on perception, shaping how people interpret other attributes of a person. Solomon Asch first systematically studied this phenomenon in 1946.Asch’s Experiment on Trait CentralityAsch's seminal study demonstrated the centrality of certain traits through a controlled experiment. Participants were presented with a...
75
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

365
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...
365
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

6.3K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
6.3K
Time-Series Graph00:54

Time-Series Graph

4.8K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.8K
Network Function of a Circuit01:25

Network Function of a Circuit

466
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.
466
Central Tendency: Analysis01:10

Central Tendency: Analysis

313
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
313

You might also read

Related Articles

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

Sort by
Same author

Role of ruxolitinib in preventing cardiac arrhythmias: A pooled analysis of clinical studies in hematology.

International journal of cardiology. Heart & vasculature·2025
Same author

Modeling shock propagation and resilience in financial temporal networks.

Chaos (Woodbury, N.Y.)·2025
Same author

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation.

Pharmaceutical statistics·2024
Same author

Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model.

Scientific reports·2022
Same author

Efficiency of the Moscow Stock Exchange before 2022.

Entropy (Basel, Switzerland)·2022
Same author

Information dynamics of price and liquidity around the 2017 Bitcoin markets crash.

Chaos (Woodbury, N.Y.)·2022
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Nov 15, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.5K

Betweenness centrality for temporal multiplexes.

Silvia Zaoli1,2, Piero Mazzarisi3,4, Fabrizio Lillo3,4

  • 1Department of Mathematics, University of Bologna, Bologna, Italy. szaoli@ictp.it.

Scientific Reports
|March 2, 2021
PubMed
Summary
This summary is machine-generated.

We introduce a new betweenness centrality metric for dynamic, multi-layered networks (temporal multiplexes). This metric improves network analysis by considering path duration and temporal structure, crucial for real-world systems like flight networks.

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K

Related Experiment Videos

Last Updated: Nov 15, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.5K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K

Area of Science:

  • Network Science
  • Graph Theory
  • Data Science

Background:

  • Standard betweenness centrality is limited to static, single-layer networks.
  • Real-world networks are often dynamic and possess multiple layers.
  • Existing metrics fail to capture the complexity of temporal multiplex networks.

Purpose of the Study:

  • To define a novel betweenness centrality for temporal multiplex networks.
  • To account for topological, temporal, and path duration aspects.
  • To provide a computational method for this new metric.

Main Methods:

  • Proposed a new definition of betweenness centrality tailored for temporal multiplexes.
  • Developed an algorithm by mapping the temporal multiplex to a static graph.
  • Applied the metric to a dataset of European flight data.

Main Results:

  • The new metric provides different rankings compared to static or single-layer metrics.
  • Analysis of European flights revealed significant changes in airport importance.
  • Highlighted the impact of temporal structure and path duration on network centrality.

Conclusions:

  • Considering the temporal multiplex structure is essential for accurate network analysis.
  • The proposed metric offers a more realistic assessment of vertex importance in dynamic, multi-layered systems.
  • This approach is vital for understanding information flow in complex, real-world networks.