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Related Concept Videos

Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

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 points...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Comparison Tests01:28

Comparison Tests

An infinite series composed of positive terms may either approach a finite value or increase without bound. Determining which outcome occurs is a central task in calculus, and comparison tests provide structured methods for making this determination. Rather than evaluating a series directly, these tests relate it to another series whose behavior is already known, allowing conclusions to be drawn through logical comparison.The direct comparison test applies to series with positive terms. If each...
Introduction to Learning01:18

Introduction to Learning

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...

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Related Experiment Video

Updated: Jul 15, 2026

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

Cluster-guided adversarial graph contrastive learning.

Dong Huang1, Jia Wan1, Zekai Zhang1

  • 1College of Mathematics and Informatics, South China Agricultural University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 13, 2026
PubMed
Summary

Graph contrastive learning (GCL) models are vulnerable to adversarial attacks. We propose Cluster-guided Adversarial Graph Contrastive Learning (CAGCL) to enhance robustness by using dual-level learning and reliable sample pairing.

Keywords:
Adversarial attack and defenseGraph contrastive learningRobust graph representation learning

Related Experiment Videos

Last Updated: Jul 15, 2026

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

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Neural Networks

Background:

  • Graph contrastive learning (GCL) excels in unsupervised graph representation learning.
  • GCL methods are susceptible to adversarial attacks, limiting their use in security-critical applications.
  • Existing methods often use single-level contrastive learning and overlook sample pair reliability, leading to overfitting structural noise.

Purpose of the Study:

  • To investigate the robustness of GCL against adversarial attacks.
  • To propose a novel approach, Cluster-guided Adversarial Graph Contrastive Learning (CAGCL), to improve GCL robustness.
  • To develop a method that learns robust and discriminative graph representations even under attack.

Main Methods:

  • Proposed Cluster-guided Adversarial Graph Contrastive Learning (CAGCL) approach.
  • Combined dual-level contrastive learning with cross-view mapping of high-confidence samples.
  • Utilized category-level guidance from reliable samples to strengthen interactions between adversarial and augmented graph views.

Main Results:

  • CAGCL demonstrated improved robustness against adversarial attacks compared to state-of-the-art baselines.
  • Experiments were conducted on multiple benchmark datasets under both untargeted and targeted attacks.
  • The proposed method learned more robust and discriminative representations.

Conclusions:

  • CAGCL effectively addresses the vulnerabilities of GCL to adversarial attacks.
  • Dual-level contrastive learning and reliable sample selection are crucial for robust graph representation learning.
  • The proposed approach enhances the practical deployment of GCL in security-sensitive domains.