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

Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

90
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...
90
Relationship Formation02:12

Relationship Formation

39.7K
What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
39.7K
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

93
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
93
Applications of Life Tables01:22

Applications of Life Tables

45
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
45
Longitudinal Studies01:26

Longitudinal Studies

120
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
120

You might also read

Related Articles

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

Sort by
Same author

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks.

Scientific reports·2024
Same author

First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal cancer.

PLoS computational biology·2023
Same author

Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks.

Science advances·2022
Same author

Quantifying ethnic segregation in cities through random walks.

Nature communications·2022
Same author

Impact of urban structure on infectious disease spreading.

Scientific reports·2022
Same author

Likelihood-based approach to discriminate mixtures of network models that vary in time.

Scientific reports·2021
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 27, 2025

Conditions Affecting Social Space in Drosophila melanogaster
08:04

Conditions Affecting Social Space in Drosophila melanogaster

Published on: November 5, 2015

12.1K

Measuring social mobility in temporal networks.

Matthew Russell Barnes1, Vincenzo Nicosia2, Richard G Clegg3

  • 1School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK. matthew.barnes@qmul.ac.uk.

Scientific Reports
|February 18, 2025
PubMed
Summary
This summary is machine-generated.

Complex networks often show a "rich-get-richer" effect. We introduce hierarchical mobility to measure how node success changes over time, revealing network dynamics.

Keywords:
HierarchyMobilityRankingTime evolving networks

More Related Videos

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K
The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

613

Related Experiment Videos

Last Updated: May 27, 2025

Conditions Affecting Social Space in Drosophila melanogaster
08:04

Conditions Affecting Social Space in Drosophila melanogaster

Published on: November 5, 2015

12.1K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K
The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

613

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Temporal Network Dynamics

Background:

  • The
  • rich-get-richer
  • phenomenon, where highly connected nodes gain more connections, is common in complex networks.
  • Static network analysis often overlooks temporal aspects of node degree evolution.
  • Existing models like preferential attachment and rich-club effects analyze static snapshots.

Purpose of the Study:

  • To introduce and define temporal measures of node success propagation in complex networks.
  • To develop a "hierarchical mobility" concept to track changes in node degree gain propensity over time.
  • To create a taxonomy of temporal correlation statistics (mobility, philanthropy, community) for network analysis.

Main Methods:

  • Defining and calculating "hierarchical mobility" based on node degree changes across time periods.
  • Introducing temporal correlation statistics: mobility, philanthropy, and community.
  • Applying these statistics to 26 real-world temporal networks and artificial network models.

Main Results:

  • Most analyzed networks exhibit stable hierarchical positions for individual nodes and their neighborhoods over time.
  • Low correlative effects were observed between individual node success and their neighborhood's success.
  • The mobility taxonomy effectively discriminates between networks from different scientific fields.

Conclusions:

  • Measuring hierarchical mobility provides valuable insights into the underlying structural dynamics of temporal networks.
  • The "rich-get-richer" effect's opposite requires degree inequality within a network.
  • Temporal analysis of network evolution offers a more nuanced understanding than static snapshots.