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

Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

302
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
302
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

281
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
281
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

18.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
18.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.3K
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...
4.3K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

529
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
529
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

852
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...
852

You might also read

Related Articles

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

Sort by
Same author

CRSwNP in the biologics era: evaluating patient-reported quality of life outcomes.

Frontiers in allergy·2026
Same author

Age-stratified associations of glycemia, blood pressure, and cholesterol with mortality in diabetes: A prospective cohort study.

BMC medicine·2026
Same author

Predictive value of thyroid autoantibodies for coronary heart disease severity in individuals with normal thyroid function based on machine learning and SHAP interpretation.

Frontiers in immunology·2026
Same author

Age-specific effects of hemoglobin A1c, blood pressure, and cholesterol levels on incident cardiovascular diseases among adults with diabetes in China: a 10-year prospective cohort study.

Life metabolism·2026
Same author

Mechanism of AGEs-RAGE axis inhibition of ferroptosis in type 2 diabetic colon cancer by regulatory CEACAM1.

Scientific reports·2026
Same author

Investigating the Role of TNFSF12 in Thyroid Cancer Progression via Single-Cell RNA Sequencing and Integrated Multiomics Analyses.

Mediators of inflammation·2026

Related Experiment Video

Updated: Feb 27, 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

4.9K

An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs.

Xu Yang, Hong Qiao, Zhi-Yong Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 11, 2017
    PubMed
    Summary

    We introduce a weighted common subgraph (WCS) matching algorithm for finding similar subgraphs in labeled weighted graphs. This method offers robust performance across various conditions, enhancing graph matching applications.

    More Related Videos

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
    12:11

    Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

    Published on: April 8, 2020

    8.8K

    Related Experiment Videos

    Last Updated: Feb 27, 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

    4.9K
    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
    12:11

    Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

    Published on: April 8, 2020

    8.8K

    Area of Science:

    • Computer Science
    • Machine Learning
    • Graph Theory

    Background:

    • Graph matching is crucial for tasks in computer vision and machine learning.
    • Existing methods like equal-sized graph matching and subgraph matching have limitations.
    • Weighted common subgraph (WCS) matching offers a generalized approach.

    Purpose of the Study:

    • To propose a novel weighted common subgraph (WCS) matching algorithm.
    • To address the challenge of finding the most similar subgraphs in labeled weighted graphs.
    • To provide a robust solution for graph matching problems.

    Main Methods:

    • Formulating WCS matching as a combinatorial optimization problem.
    • Utilizing partial permutation matrices to represent subgraph correspondences.
    • Applying the graduated nonconvexity and concavity procedure for approximate optimization.

    Main Results:

    • The proposed WCS matching algorithm demonstrates effectiveness in identifying similar subgraphs.
    • Experimental results validate the algorithm's robustness against noise, size, outliers, and edge density.
    • Successful comparisons on both synthetic and real-world image datasets.

    Conclusions:

    • The developed WCS matching algorithm is a powerful tool for subgraph matching.
    • The approach offers significant advantages in terms of robustness and applicability.
    • This method advances graph matching techniques in computer vision and machine learning.