图自监督学习综述

A Survey on Graph Self-supervised Learning

关键词:  自监督学习,图神经网络,图表示学习,深度学习,图分析,综述

Key words:  Self-supervised  learning, Graph  Neural Networks, Graph  Representation Learning, Deep  Learning, Graph  Analysis, Review.

摘要:

图深度学习最近引起了研究者们的兴趣。由于人工标注的高昂成本和监督学习的泛化能力不足的特点,图自监督学习(Graph Self-Supervised Learning, GSSL)通过精心设计的代理任务提取信息知识,而不依赖于人工标签,已经成为一种有前途和趋势的图数据学习范式。本综述论文全面探讨了图自监督学习的最新进展和挑战。本文首先介绍了图自监督学习的基本概念,包括其与传统图学习方法的区别和联系。随后,本文详细回顾了当前图自监督学习的主要方法,并将其分为四个大类:图自预测,对比学习,不变挖掘,混合方法。此外,文章还探讨和总结了图自监督学习在医学图像上的应用。最后,本文提出了未来研究的方向,包括提高算法的可解释性、扩展到更复杂的图结构。通过这篇综述,读者可以获得对图自监督学习领域的全面了解,以及该领域当前面临的机遇和挑战。

Abstract:

Graph deep learning has recently garnered interest among researchers. Given the high cost of manual annotation and the limited generalization capability of supervised learning, Graph Self-Supervised Learning (GSSL), which extracts information knowledge through carefully designed pretext tasks without relying on manual labels, has emerged as a promising and trending paradigm in graph deep learning. In this paper, we comprehensively explore the latest advancements and challenges in graph self-supervised learning. We first introduce the basic concepts of graph self-supervised learning, including its differences and connections with traditional graph learning methods. It then thoroughly reviews the current main methods of graph self-supervised learning, categorizing them into four types: graph self-prediction, contrastive learning, invariant mining, and hybrid methods. Additionally, we discuss and summarize applications of graph self-supervised learning in medical imaging. Finally, we propose future research directions, including improving algorithm interpretability and extending to more complex graph structures. Through this review, readers can gain a comprehensive understanding of the field of graph self-supervised learning, as well as the opportunities and challenges currently facing this area.