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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.

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.

Keywords:
adversarial trainingcontrastive learninggraph representation learningmutual information

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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.

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.