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

Factors Influencing Attraction I: Proximity01:22

Factors Influencing Attraction I: Proximity

318
Proximity plays a fundamental role in shaping interpersonal attraction by increasing opportunities for interaction and fostering familiarity. Research consistently demonstrates that individuals are more likely to form social bonds with those who are physically closer to them, whether in residential settings, workplaces, or educational institutions. This effect is largely driven by the increased frequency of encounters, which facilitates the development of friendships and romantic...
318
Factors Influencing Attraction V: Social Skills01:29

Factors Influencing Attraction V: Social Skills

862
Social skills play a crucial role in shaping interpersonal interactions and enhancing individuals' ability to navigate various social environments successfully. These skills contribute to personal and professional success, influencing how others perceive and treat individuals. High social skills provide distinct advantages in numerous settings, including romantic relationships, politics, and legal proceedings. In courtroom settings, for instance, defendants who exhibit strong social skills are...
862
Factors Influencing Attraction II: Physical Attraction01:21

Factors Influencing Attraction II: Physical Attraction

297
Physical attractiveness plays a crucial role in shaping interpersonal attraction, influencing first impressions, social interactions, and long-term relationship dynamics. Psychological research consistently demonstrates that attractiveness affects social evaluations and behavioral outcomes in various contexts.Influence on Social InteractionsResearch has shown that individuals perceived as physically attractive often experience preferential treatment in social and professional settings. One...
297
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

854
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
854
Factors Influencing Attraction IV: Reciprocity01:28

Factors Influencing Attraction IV: Reciprocity

325
Reciprocity in attraction is fundamental to social and romantic relationships, shaping how individuals form and maintain connections. The psychological principle underlying this phenomenon is that people tend to like those who express liking toward them. Balance theory supports this tendency, suggesting that mutual attraction fosters psychological harmony, whereas one-sided affection leads to discomfort and cognitive dissonance.The Psychological Mechanisms Behind ReciprocityWhen individuals...
325
Local Attraction01:22

Local Attraction

434
Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
434

You might also read

Related Articles

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

Sort by
Same author

Effects of restraint tether devices on pilots' neck injuries under arrested landing conditions.

Computer methods in biomechanics and biomedical engineering·2026
Same author

A Survey of Modern Data Acquisition and Analysis Systems for Environmental Risk Monitoring in Aquatic Ecosystems.

Sensors (Basel, Switzerland)·2026
Same author

A finite element study on three maxillary protraction devices for treating maxillary sagittal hypoplasia at different levels of bone fusion.

Frontiers in bioengineering and biotechnology·2026
Same author

Performance evaluation of RespiCast ensemble forecasts for primary care syndromic indicators of viral respiratory disease in Europe.

medRxiv : the preprint server for health sciences·2026
Same author

How malicious AI swarms can threaten democracy.

Science (New York, N.Y.)·2026
Same author

Controlling the spread of deception-based cyber-threats on time-varying networks.

Physical review. E·2026

Related Experiment Video

Updated: Feb 28, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

Random walks on activity-driven networks with attractiveness.

Laura Alessandretti1, Kaiyuan Sun2, Andrea Baronchelli1

  • 1Department of Mathematics, City University of London, Northampton Square, London EC1V 0HB, United Kingdom.

Physical Review. E
|June 17, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic network model accounting for node activity and attractiveness. It reveals how these heterogeneous network features significantly impact random-walk processes, crucial for understanding real-world systems.

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

1.6K
Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
08:19

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

9.4K

Related Experiment Videos

Last Updated: Feb 28, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
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

1.6K
Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
08:19

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion

Published on: January 15, 2016

9.4K

Area of Science:

  • Complex Systems and Network Science
  • Statistical Physics
  • Social Network Analysis

Background:

  • Real-world networks are dynamic, not static structures.
  • Node interactions in social networks are influenced by activity (propensity to interact) and attractiveness (selection probability).
  • These features are often heterogeneously distributed across nodes.

Purpose of the Study:

  • To develop a time-varying network model incorporating heterogeneous activity and attractiveness.
  • To investigate the impact of these dynamic network properties on random-walk processes.
  • To analyze scenarios where network evolution and process timescales are comparable.

Main Methods:

  • Development of a novel time-varying network model.
  • Analytical derivation of solutions for stationary states and mean first-passage times.
  • Empirical validation using social network data.

Main Results:

  • The model captures heterogeneous distributions of node activity and attractiveness.
  • Analytical solutions were obtained for random-walk dynamics on these evolving networks.
  • Heterogeneity in activity and attractiveness, and their correlations, significantly alter network processes.

Conclusions:

  • Dynamic network properties, particularly heterogeneous activity and attractiveness, are critical for understanding network processes.
  • Previous models often overlook these factors, leading to incomplete analyses.
  • The findings have implications for modeling information diffusion, disease spread, and other phenomena on social networks.