Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Relationship Formation

Relationship Formation

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,...

Factors Influencing Attraction I: Proximity

Factors Influencing Attraction I: Proximity

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...

Factors Influencing Attraction III: Similarity

Factors Influencing Attraction III: Similarity

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...

Causes of Similarity-Dissimilarity Effect

Causes of Similarity-Dissimilarity Effect

The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...

Social Exchange Theory

Social Exchange Theory

As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...

You might also read

Related Articles

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

Sort by

Same author

Extracting the transitivity backbone of bipartite networks.

Nature communications·2026

Same author

HypBench: Hyperbolic Benchmark for Graph Neural Network Performance.

IEEE transactions on neural networks and learning systems·2026

Same author

Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis.

Nature communications·2026

Same author

Structural signatures of synergy and redundancy in human brain function.

bioRxiv : the preprint server for biology·2026

Same author

Clustering Does Not Always Imply Latent Geometry.

Physical review letters·2025

Same author

The multiscale self-similarity of the weighted human brain connectome.

PLoS computational biology·2025

Search research articles

Related Experiment Video

Updated: May 3, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Popularity versus similarity in growing networks.

Fragkiskos Papadopoulos¹, Maksim Kitsak, M Ángeles Serrano

¹Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 33 Saripolou Street, 3036 Limassol, Cyprus. f.papadopoulos@cut.ac.cy

|September 14, 2012

Summary

New network growth models show that attractiveness depends on both popularity and similarity, not just popularity. This new framework accurately predicts link formation in technological, social, and biological networks.

More Related Videos

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

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

Published on: December 7, 2021

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 3, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

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

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

Published on: December 7, 2021

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Network Science
Complex Systems
Mathematical Modeling

Background:

Preferential attachment, where popular nodes attract more connections, explains network scaling.
This principle is observed in various real-world networks like the Internet.
It can arise from factors like node fitness, ranking, or optimization.

Purpose of the Study:

To challenge the sole reliance on popularity in network growth models.
To introduce a new framework incorporating both popularity and similarity for node attractiveness.
To provide a more accurate model for evolving networks.

Main Methods:

Developed a novel network evolution framework optimizing trade-offs between node popularity and similarity.
Utilized a geometric interpretation to demonstrate how popularity preference emerges from local optimization.
Validated the framework against large-scale data from technological, social, and biological networks.

Main Results:

The proposed optimization framework accurately describes the evolution of diverse networks, including the Internet, social trust networks, and E. coli metabolic networks.
It predicts the probability of new link formation with high precision, outperforming traditional preferential attachment models.
Demonstrated that popularity is only one facet of attractiveness, with similarity playing a crucial role.

Conclusions:

Node attractiveness in growing networks is a multi-dimensional concept, balancing popularity and similarity.
The developed optimization framework offers a more comprehensive explanation for network evolution than preferential attachment alone.
This framework has significant implications for predicting future links and understanding emergent network phenomena.