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

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by

Same author

Growth of Sphagnum is strongly rhythmic: contribution of the seasonal, circalunar and third components.

Physiologia plantarum·2019

Same author

Revisiting random walk based sampling in networks: evasion of burn-in period and frequent regenerations.

Computational social networks·2018

Same journal

Graph convolutional networks: a comprehensive review.

Computational social networks·2023

Same journal

Modeling the transmission dynamics of racism propagation with community resilience.

Computational social networks·2021

Same journal

A model for the co-evolution of dynamic social networks and infectious disease dynamics.

Computational social networks·2021

Same journal

Influence spreading model used to analyse social networks and detect sub-communities.

Computational social networks·2018

Same journal

Social learning for resilient data fusion against data falsification attacks.

Computational social networks·2018

Same journal

Tracking online topics over time: understanding dynamic hashtag communities.

Computational social networks·2018

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 2, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

Network partitioning algorithms as cooperative games.

Konstantin E Avrachenkov¹, Aleksei Y Kondratev^2,3, Vladimir V Mazalov^3,4

¹1Inria Sophia Antipolis, 2004 Route des Lucioles, 06902 Valbonne, France.

Computational Social Networks

|November 13, 2018

Summary

This summary is machine-generated.

This study introduces game-theoretic methods for network community detection, using cooperative game theory to reveal cluster formation mechanisms beyond simple link density. It offers intuitive resolution tuning and efficient computation for detecting network communities.

Keywords:

Community detection Cooperative game Gibbs sampling Hedonic game Myerson value Network partitioning

More Related Videos

Measuring Engagement of Spectators of Social Digital Games

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

Stereoacuity Improvement using Random-Dot Video Games

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

Related Experiment Videos

Last Updated: Feb 2, 2026

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

Measuring Engagement of Spectators of Social Digital Games

Measuring Engagement of Spectators of Social Digital Games

Published on: July 3, 2021

Stereoacuity Improvement using Random-Dot Video Games

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

Area of Science:

Network Science
Game Theory
Computational Social Science

Background:

Traditional community detection methods focus on subgraph density.
Understanding cluster formation mechanisms is crucial for network analysis.

Purpose of the Study:

To apply cooperative game theory for enhanced community detection in networks.
To introduce novel methods based on Myerson value and hedonic games.
To provide an intuitive framework for tuning community detection resolution.

Main Methods:

Application of cooperative game theory, specifically Myerson value and hedonic games.
Analysis of cluster formation mechanisms and link density.
Development of an efficient computational scheme using Gibbs sampling for potential hedonic games.

Main Results:

Two game-theoretic approaches for community detection are proposed.
Both methods effectively detect clusters at various resolutions.
Hedonic games framework unifies existing methods like modularity, ratio cut, and normalized cut.
An efficient computational scheme is presented for potential hedonic games.

Conclusions:

Game-theoretic methods offer a powerful alternative for network community detection.
Cooperative game theory provides insights into cluster formation beyond density.
Hedonic games present a flexible and unified approach to community detection.