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

Protein Networks02:26

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,...
Protein Networks02:26

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,...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Graphs of Two-Variable Functions01:27

Graphs of Two-Variable Functions

A weather map provides a practical example of a function of two variables. Across a wide region such as the United States, temperatures vary from one location to another. Each location can be identified by two geographic coordinates: longitude and latitude. Since a single temperature value is assigned to each coordinate pair, the situation can be represented mathematically as a function with two inputs and one output.In mathematical notation, longitude and latitude can be labeled as x and y,...

You might also read

Related Articles

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

Sort by
Same author

Pipa Qingfei Yin Ameliorates Acne Inflammation in Mice via Suppressing Neutrophil Extracellular Traps.

Microbiology and immunology·2026
Same author

Exosome-derived LncRNAs in bone remodeling: recent advances and future directions for bone disease therapy.

Stem cell research & therapy·2026
Same author

Exploring the Lung-Liver Axis in Pulmonary Arterial Hypertension.

Comprehensive Physiology·2026
Same author

Avian lung single-cell atlas elucidates evolutionary divergence in endothermic respiration.

Molecular biology and evolution·2026
Same author

Enhanced adsorption removal of fluoride by multifunctional polymer-based hydrated neodymium oxide: Capacity evaluation and mechanism.

Journal of hazardous materials·2026
Same author

CellLoop: Identifying single-cell 3D genome chromatin loops.

Nature communications·2026

Related Experiment Video

Updated: Jul 10, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

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

Published on: September 25, 2021

Uncovering fuzzy community structure in complex networks.

Shihua Zhang1, Rui-Sheng Wang, Xiang-Sun Zhang

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China. zsh@amss.ac.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2007
PubMed
Summary

This study introduces a novel fuzzy community detection method using non-negative matrix factorization (NMF). The technique quantifies node membership, revealing complex network structures effectively.

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

Related Experiment Videos

Last Updated: Jul 10, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

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

Published on: September 25, 2021

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

Area of Science:

  • Complex networks analysis
  • Graph theory
  • Data mining

Background:

  • Real-world networks exhibit common properties like small-world phenomena and power-law distributions.
  • Detecting community structures is crucial for understanding network organization.
  • Existing methods may struggle with overlapping or fuzzy community memberships.

Purpose of the Study:

  • To present a novel community detection method for complex networks.
  • To develop a technique capable of identifying fuzzy communities where nodes can belong to multiple groups.
  • To quantify the degree of membership for each node within detected communities.

Main Methods:

  • Utilized non-negative matrix factorization (NMF) for community detection.
  • Employed a diffusion kernel to derive a feature matrix.
  • Integrated a popular modular function to guide the NMF algorithm.

Main Results:

  • The method successfully detects an appropriate number of fuzzy communities.
  • It quantifies the absolute membership degree of each node to each community.
  • Validated performance on both artificial and real-world network datasets.

Conclusions:

  • The proposed NMF-based method effectively uncovers fuzzy community structures in complex networks.
  • The quantification of absolute membership degrees offers a significant advantage over traditional methods.
  • The approach demonstrates robustness and applicability to diverse network types.