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

Fundamental Attribution Error

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...

What are Populations and Communities?

What are Populations and Communities?

Community Based Intervention

Community Based Intervention

Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...

In- and Out-Groups

In- and Out-Groups

People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.

Social Proof

Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

You might also read

Related Articles

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

Sort by

Same author

Visualizing epidemiological models for policy: design principles for effective communication.

Frontiers in public health·2026

Same author

ISilDR: Isometric Seriation-Based Dimensionality Reduction for Visual Cluster Analysis.

IEEE transactions on visualization and computer graphics·2026

Same author

Bundling-Aware Graph Drawing Revisited.

IEEE transactions on visualization and computer graphics·2025

Same author

Embarrassingly Agile-Data Visualization Methodology in Emergency Responses.

IEEE computer graphics and applications·2025

Same author

Knowledge transfer, knowledge gaps, and knowledge silos in citation networks.

PloS one·2025

Same author

CowMesh: a data-mesh architecture to unify dairy industry data for prediction and monitoring.

Frontiers in artificial intelligence·2023

Same journal

Changes in patient-sharing patterns after oncologist departures in rural and urban settings: a Medicare cohort study.

Applied network science·2026

Same journal

Tunable network properties with Hamill and Gilbert's Social Circles generator.

Applied network science·2025

Same journal

Initialisation and network effects in decentralised federated learning.

Applied network science·2025

Same journal

The association of prescriber prominence in a shared-patient physician network with their patients receipt of and transitions between risky drug combinations.

Applied network science·2025

Same journal

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation.

Applied network science·2025

Same journal

Navigation on temporal networks.

Applied network science·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 17, 2025

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Towards explainable community finding.

Sophie Sadler¹, Derek Greene², Daniel Archambault¹

¹Swansea University, Swansea, UK.

Applied Network Science

|December 13, 2022

Summary

This summary is machine-generated.

This study introduces a new method to explain network community detection. It uses interpretable features to understand why algorithms group nodes, aiding in network analysis and public health applications.

Keywords:

Community detection Explainability Graph mining Network analysis

More Related Videos

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

Related Experiment Videos

Last Updated: Aug 17, 2025

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

Area of Science:

Network Science
Machine Learning
Data Mining

Background:

Community detection is crucial for network analysis, with many algorithms used in fields like public health.
Existing methods often lack clear explanations for their community assignments.
Understanding the reasoning behind community labels is essential for trust and application.

Purpose of the Study:

To develop a model-agnostic methodology for post-hoc explanations of community detection algorithms.
To identify informative network features that elucidate community structures.
To provide insights into the commonalities and differences between various community detection approaches.

Main Methods:

Proposing a novel methodology inspired by machine learning interpretability techniques.
Applying the methodology to explain outputs from three well-established community detection algorithms.
Identifying and analyzing a set of interpretable network features.

Main Results:

The methodology successfully provides post-hoc explanations for community detection algorithms.
Key network features contributing to community assignments were identified.
Common and distinct explanatory features across different algorithms were reported.

Conclusions:

The proposed methodology offers a generalizable approach to explain community detection outputs.
Understanding the features driving community formation enhances the interpretability and applicability of network analysis.
This work bridges the gap between complex algorithms and actionable insights in network science.