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AutoSGRL: Automated framework construction for self-supervised graph representation learning.

Yu Xie1, Yu Chang1, Ming Li2

  • 1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces AutoSGRL, a novel method for automatically building self-supervised graph representation learning frameworks. AutoSGRL optimizes graph contrastive learning using genetic algorithms, outperforming manual designs.

Keywords:
Automated machine learningGraph representation learningSelf-supervised contrastive learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Representation Learning

Background:

  • Automated machine learning (AutoML) is advancing AI development.
  • Current graph neural architecture search primarily focuses on supervised or semi-supervised learning.
  • A gap exists in automated methods for self-supervised graph representation learning.

Purpose of the Study:

  • To propose AutoSGRL, the first method for automatic construction of flexible self-supervised graph representation learning frameworks.
  • To establish a comprehensive search space for self-supervised graph representation learning, including data augmentation, proxy tasks, and hyperparameters.
  • To develop an automated search engine for optimizing these frameworks.

Main Methods:

  • Developed AutoSGRL, a framework leveraging existing self-supervised graph contrastive learning methods.
  • Defined a search space encompassing data augmentation, proxy tasks, and hyperparameters for graph contrastive learning.
  • Implemented a genetic algorithm-based search engine simulating biological evolution (selection, crossover, mutation) to iteratively optimize frameworks.

Main Results:

  • AutoSGRL successfully constructs self-supervised graph representation learning frameworks.
  • The proposed method achieves performance comparable to or better than state-of-the-art manual methods.
  • Demonstrated effectiveness against both manual self-supervised methods and semi-supervised graph neural architecture search.

Conclusions:

  • AutoSGRL represents a significant advancement in automated graph representation learning.
  • The method offers a flexible and effective approach to building self-supervised graph learning frameworks.
  • AutoSGRL paves the way for more accessible and powerful graph representation learning solutions.