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

88
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...
88
Entropy02:39

Entropy

33.7K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
33.7K
Entropy01:18

Entropy

3.3K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.3K
What is Central Tendency?01:14

What is Central Tendency?

16.8K
Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large quantities of data and developing an inference, one needs to identify a value representative of the entire data set. Characteristics such as central tendency, extreme values, range of measurements, or the most repeated value can help better understand the data.
The central tendency is the most conventionally used data characteristic. It is a...
16.8K
Probability Histograms01:17

Probability Histograms

12.9K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.9K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
4.0K

You might also read

Related Articles

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

Sort by
Same author

The interplay between ecological networks drives host-plasmid community dynamics.

PLoS computational biology·2026
Same author

Reducibility of higher-order networks from dynamics.

Nature communications·2026
Same author

Unraveling the Network Signatures of Oncogenicity in Virus-Human Protein-Protein Interactions.

Entropy (Basel, Switzerland)·2025
Same author

Decoding the architecture of living systems.

Reports on progress in physics. Physical Society (Great Britain)·2025
Same author

Bifurcations and phase transitions in the origins of life.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2025
Same author

A cognitive multiplex network approach to investigate mental navigation and predict high-level cognition.

Behavior research methods·2025

Related Experiment Video

Updated: Nov 27, 2025

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.0K

Distance Entropy Cartography Characterises Centrality in Complex Networks.

Massimo Stella1, Manlio De Domenico1

  • 1Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo, Italy.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

We introduce distance entropy, a new measure for complex networks, to better predict how toddlers learn words by analyzing language networks. This method improves upon traditional closeness centrality in network analysis.

Keywords:
closeness centralitycomplex networksentropymultiplex lexical networksnetwork measures

More Related Videos

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

1.4K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.5K

Related Experiment Videos

Last Updated: Nov 27, 2025

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.0K
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

1.4K
Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.5K

Area of Science:

  • Network science
  • Information theory
  • Developmental psychology

Background:

  • Complex networks are ubiquitous in nature and society.
  • Understanding node importance (centrality) is crucial for network analysis.
  • Existing centrality measures may not fully capture local network structure.

Purpose of the Study:

  • Introduce distance entropy as a novel centrality measure for complex networks.
  • Develop a network cartography combining distance entropy and closeness centrality.
  • Evaluate the predictive power of this cartography for child language acquisition.

Main Methods:

  • Define and analyze properties of distance entropy on synthetic network models.
  • Couple distance entropy with closeness centrality to create a network cartography.
  • Apply the cartography to an empirical multiplex lexical network of toddlers' vocabulary.

Main Results:

  • Distance entropy quantifies homogeneity in path length distributions.
  • The combined cartography reduces ranking degeneracy compared to closeness centrality alone.
  • Distance entropy cartography shows superior prediction of word learning in toddlers.

Conclusions:

  • Distance entropy offers valuable insights into local network structure.
  • Network cartography using distance entropy enhances understanding of complex systems.
  • This approach has significant implications for studying language acquisition and other network-dependent processes.