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 Experiment Video

Updated: May 10, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

8.8K

ArieL: Adversarial Graph Contrastive Learning.

Shengyu Feng1, Baoyu Jing2, Yada Zhu3

  • 1Carnegie Mellon University, USA.

ACM Transactions on Knowledge Discovery From Data
|April 23, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Associative Learning01:27

Associative Learning

239
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...
239
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.5K
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...
11.5K
Observational Learning01:12

Observational Learning

98
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...
98
Introduction to Learning01:18

Introduction to Learning

302
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...
302
Cognitive Learning01:21

Cognitive Learning

94
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...
94
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

255
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
255

You might also read

Related Articles

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

Sort by
Same journal

ProcessGAN: Generating Privacy-Preserving Time-Aware Process Data with Conditional Generative Adversarial Nets.

ACM transactions on knowledge discovery from data·2025
Same journal

Bayesian Variable Selection in Linear Regression in One Pass for Large Data Sets.

ACM transactions on knowledge discovery from data·2023
Same journal

Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping.

ACM transactions on knowledge discovery from data·2019
Same journal

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

ACM transactions on knowledge discovery from data·2017
Same journal

CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering.

ACM transactions on knowledge discovery from data·2017
Same journal

Scalable and Axiomatic Ranking of Network Role Similarity.

ACM transactions on knowledge discovery from data·2014
See all related articles

Adversarial Graph Contrastive Learning (ArieL) introduces an adversarial view for data augmentation, improving unsupervised graph representation learning. This method generates high-quality contrastive samples, outperforming existing techniques in node and graph classification tasks.

Area of Science:

  • Graph Representation Learning
  • Unsupervised Learning
  • Machine Learning

Background:

  • Contrastive learning is key for unsupervised graph representation learning, relying on positive and negative sample construction.
  • Existing methods often use node proximity, while data augmentation from computer vision shows promise but is challenging for graphs.
  • Generating high-quality contrastive samples for graph data augmentation remains an open area for improvement.

Purpose of the Study:

  • To propose a simple yet effective method, Adversarial Graph Contrastive Learning (ArieL), for extracting informative contrastive samples in graph representation learning.
  • To address the challenges of data augmentation in graph domains by introducing an adversarial graph view.
  • To generalize the proposed method for both node-level and graph-level contrastive learning.
Keywords:
adversarial trainingcontrastive learninggraph representation learningmutual information

More Related Videos

Static Adhesion Assay for the Study of Integrin Activation in T Lymphocytes
09:14

Static Adhesion Assay for the Study of Integrin Activation in T Lymphocytes

Published on: June 13, 2014

15.9K
Axonal Transport of Organelles in Motor Neuron Cultures using Microfluidic Chambers System
10:12

Axonal Transport of Organelles in Motor Neuron Cultures using Microfluidic Chambers System

Published on: May 5, 2020

9.9K

Related Experiment Videos

Last Updated: May 10, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

8.8K
Static Adhesion Assay for the Study of Integrin Activation in T Lymphocytes
09:14

Static Adhesion Assay for the Study of Integrin Activation in T Lymphocytes

Published on: June 13, 2014

15.9K
Axonal Transport of Organelles in Motor Neuron Cultures using Microfluidic Chambers System
10:12

Axonal Transport of Organelles in Motor Neuron Cultures using Microfluidic Chambers System

Published on: May 5, 2020

9.9K

Main Methods:

  • Introduced an adversarial graph view for data augmentation to generate informative contrastive samples.
  • Developed information regularization for stable training and subgraph sampling for scalability.
  • Generalized the approach from node-level to graph-level contrastive learning by treating graphs as super-nodes.

Main Results:

  • ArieL consistently outperformed current graph contrastive learning methods on real-world datasets for both node-level and graph-level classification.
  • Demonstrated enhanced robustness of ArieL against adversarial attacks.
  • Successfully extracted high-quality contrastive samples within reasonable constraints.

Conclusions:

  • Adversarial Graph Contrastive Learning (ArieL) offers a powerful new approach for unsupervised graph representation learning.
  • The method effectively addresses limitations in graph data augmentation for contrastive learning.
  • ArieL shows significant potential for improving graph classification tasks and robustness.