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

What are Populations and Communities?

What are Populations and Communities?

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.

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Affinity and Avidity

Affinity and Avidity

Social Loafing

Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...

Region of Convergence

Region of Convergence

The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...

You might also read

Related Articles

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

Sort by

Same author

Prediction of Adverse Events in Single Ventricle Physiology Infants Using Artificial Intelligence Tools.

Critical care explorations·2026

Same author

Building a sustainable neurocardiac critical care program in a paediatric cardiac ICU: insights and lessons learned.

Cardiology in the young·2025

Same author

High-Performance Concrete from Rubber and Shell Waste Materials: Experimental and Computational Analysis.

Materials (Basel, Switzerland)·2024

Same author

Impact of an Adult Congenital Heart Disease Order Set in a Pediatric Cardiac Intensive Care Unit.

World journal for pediatric & congenital heart surgery·2024

Same author

Pediatric Tele-Critical Care: Initial Experience with a Continuous Surveillance Model Aiming to Prevent Cardiac Arrest in Children with Critical Heart Disease.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association·2024

Same author

Correlation dimension of high-dimensional and high-definition experimental time series.

Chaos (Woodbury, N.Y.)·2023

Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026

Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026

Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026

Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026

Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026

Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 25, 2026

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

Content patterns in topic-based overlapping communities.

Sebastián A Ríos¹, Ricardo Muñoz¹

¹Industrial Engineering Department, University of Chile, Av. República 701, 8370439 Santiago, Chile.

Thescientificworldjournal

|August 28, 2014

Summary

This summary is machine-generated.

This study introduces a new method for identifying overlapping communities in social networks by combining network structure and user post content. The hybrid algorithm effectively detects complex community structures and analyzes content patterns, outperforming existing methods.

More Related Videos

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Related Experiment Videos

Last Updated: Apr 25, 2026

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

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Area of Science:

Social Network Analysis
Computational Social Science
Data Mining

Background:

Detecting community structure is crucial in social network analysis.
Current methods often fail to account for overlapping memberships and textual content.
Existing algorithms typically focus on disjoint communities based solely on network topology.

Purpose of the Study:

To develop a novel methodology for detecting overlapping subcommunities in online social networks.
To create a method for analyzing content patterns within these subcommunities using topic models.
To present a hybrid algorithm combining topology-based and topic-based approaches.

Main Methods:

Developed a hybrid algorithm integrating graph structure (topology-based) and textual information (topic-based) for subcommunity detection.
Employed topic models to analyze and compare content patterns within detected subcommunities.
Tested the algorithm on real-world virtual community data.

Main Results:

The proposed hybrid algorithm successfully detects overlapping subcommunities.
The method effectively analyzes and compares content generated by different subcommunities.
Empirical tests demonstrate superior performance compared to existing algorithms.

Conclusions:

The novel hybrid approach enhances the understanding of complex community structures in social networks.
Integrating network topology and user-generated content provides a more comprehensive analysis.
This methodology offers a significant advancement in social network analysis and community detection.