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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

14.8K
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...
14.8K
Review and Preview01:13

Review and Preview

9.7K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
9.7K
Introduction to Learning01:18

Introduction to Learning

577
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
577
Multiple Bar Graph01:07

Multiple Bar Graph

8.2K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.2K
Classification of Systems-I01:26

Classification of Systems-I

346
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
346

You might also read

Related Articles

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

Sort by
Same author

SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.

Nature communications·2026
Same author

A Survey on Vision-Language-Action Models for Embodied AI.

IEEE transactions on neural networks and learning systems·2026
Same author

HC-GLAD: Dual hyperbolic contrastive learning for unsupervised graph-level anomaly detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Therapeutic and prognostic value of repeat transurethral resection for high-grade Ta bladder cancer: a propensity score matching analysis.

American journal of cancer research·2026
Same author

KnitLoRA: bridging low-rank adaptation as interwoven layers for deeper semantic reasoning.

Scientific reports·2026
Same author

Recent Advances of Multimodal Continual Learning: A Comprehensive Survey.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Sep 29, 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

634

Graph-Based Semi-Supervised Learning: A Comprehensive Review.

Zixing Song, Xiangli Yang, Zenglin Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |March 18, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study offers a systematic understanding of graph-based semi-supervised learning (GSSL), a key machine learning technique using labeled and unlabeled data. It introduces a new taxonomy and resources for researchers and practitioners in this rapidly advancing field.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    665
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    538

    Related Experiment Videos

    Last Updated: Sep 29, 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

    634
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    665
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    538

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Semi-supervised learning (SSL) leverages both labeled and unlabeled data for enhanced model training.
    • Graph-based semi-supervised learning (GSSL) methods infer labels by analyzing data structures within affinity graphs.
    • GSSL methods offer advantages in structure, broad applicability, and scalability for large datasets.

    Purpose of the Study:

    • To provide a comprehensive and systematic overview of Graph-Based Semi-Supervised Learning (GSSL) methods.
    • To distinguish this work by focusing exclusively on GSSL, unlike broader SSL surveys.
    • To offer researchers and practitioners a unified framework, updated references, and practical resources.

    Main Methods:

    • Focuses exclusively on Graph-Based Semi-Supervised Learning (GSSL) methodologies.
    • Develops a newly generalized taxonomy for GSSL within a unified framework.
    • Compiles up-to-date references, code, datasets, and application examples.

    Main Results:

    • Presents a distinct and focused review of GSSL techniques.
    • Introduces a novel, generalized taxonomy for classifying GSSL methods.
    • Consolidates valuable resources including code, datasets, and application domains.

    Conclusions:

    • This work provides a foundational understanding of GSSL, distinct from general SSL surveys.
    • The proposed taxonomy and resources facilitate deeper insights and future research in GSSL.
    • Identifies potential research directions, contributing to the growth of the GSSL field.