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 Functions01:30

Graphs of Functions

33
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
33
Bar Graph01:07

Bar Graph

20.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
20.5K
Ogive Graph01:07

Ogive Graph

6.2K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.2K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.8K
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...
15.8K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

41
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...
41
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

48
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...
48

You might also read

Related Articles

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

Sort by
Same author

A Novel Strategy for Oxidative Damage: Lycopene Nanoformulations Improve Redox Homeostasis and Gut Microbiota Composition in Mice.

Journal of food science·2026
Same author

High-Level Production of a <i>Rhizomucor miehei</i> Lipase Variant in <i>Komagataella phaffii</i> via Gene Dosage Optimization and Ribosomal Engineering for Biodiesel Synthesis.

Journal of agricultural and food chemistry·2026
Same author

Predicting trajectories of illness using RNA velocity of whole blood.

Nature communications·2026
Same author

Evaluation of large language models for PI-RADS score extraction from free-text prostate MRI reports: a comparative study with human readers.

Frontiers in oncology·2026
Same author

One-Hour Plasma Glucose and Early Cardiometabolic Risk in Normoglycaemic Women With Polycystic Ovary Syndrome: A Retrospective Cross-Sectional Study.

BJOG : an international journal of obstetrics and gynaecology·2026
Same author

One-month early time-restricted eating mitigates brain aging and enhances memory in males with metabolic syndrome: an MRI structural study.

Frontiers in aging·2026
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Nov 5, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

835

Geometric graphs from data to aid classification tasks with Graph Convolutional Networks.

Yifan Qian1, Paul Expert2,3, Pietro Panzarasa1

  • 1School of Business and Management, Queen Mary University of London, London, UK.

Patterns (New York, N.Y.)
|May 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to enhance classification accuracy by creating geometric graphs from data features, even without pre-existing relational information. These graphs improve data alignment and class separation within Graph Convolutional Networks.

Keywords:
Graph Convolutional NetworksGraph Neural Networksclassification tasksdata sciencegeometric deep learninggraph constructiongraph sparsificationgraph theorymachine learningnetwork science

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K

Related Experiment Videos

Last Updated: Nov 5, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

835
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K

Area of Science:

  • Machine Learning
  • Data Science
  • Computer Science

Background:

  • Traditional classification relies solely on sample features.
  • Evolving paradigms incorporate relational information between samples.
  • Relational data is not always available for classification tasks.

Purpose of the Study:

  • To improve classification accuracy by constructing geometric graphs from features.
  • To leverage Graph Convolutional Networks (GCNs) with feature-derived graphs.
  • To demonstrate the effectiveness of this approach even without explicit relational data.

Main Methods:

  • Constructing geometric graphs directly from data features.
  • Utilizing Graph Convolutional Networks (GCNs) for classification.
  • Applying spectral sparsification to optimize graph efficiency.

Main Results:

  • Classification accuracy improved by using feature-derived graphs.
  • Optimal graphs exhibited low edge density, capturing sample similarity.
  • Graphs enhanced data-to-ground truth alignment and class separation.
  • Spectral sparsification maintained performance while reducing graph complexity.

Conclusions:

  • Geometric graphs constructed from features can significantly boost classification performance.
  • Low-density, similarity-based graphs are most effective.
  • This method offers an efficient way to improve classification in various scientific domains.