Graph Self-Supervised Learning: A Survey

Loading...
Thumbnail Image
File version

Accepted Manuscript (AM)

Author(s)
Liu, Y
Jin, M
Pan, S
Zhou, C
Zheng, Y
Xia, F
Yu, P
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2022
Size
File type(s)
Location
License
Abstract

Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL), which extracts informative knowledge through well-designed pretext tasks without relying on manual labels, has become a promising and trending learning paradigm for graph data. Different from SSL on other domains like computer vision and natural language processing, SSL on graphs has an exclusive background, design ideas, and taxonomies. Under the umbrella of graph self-supervised learning, we present a timely and comprehensive review of the existing approaches which employ SSL techniques for graph data. We construct a unified framework that mathematically formalizes the paradigm of graph SSL. According to the objectives of pretext tasks, we divide these approaches into four categories: generation-based, auxiliary property-based, contrast-based, and hybrid approaches. We further describe the applications of graph SSL across various research fields and summarize the commonly used datasets, evaluation benchmark, performance comparison and open-source codes of graph SSL. Finally, we discuss the remaining challenges and potential future directions in this research field.

Journal Title

IEEE Transactions on Knowledge and Data Engineering

Conference Title
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Item Access Status
Note

This publication has been entered in Griffith Research Online as an advanced online version.

Access the data
Related item(s)
Subject

Data engineering and data science

Data mining and knowledge discovery

Information and computing sciences

Persistent link to this record
Citation

Liu, Y; Jin, M; Pan, S; Zhou, C; Zheng, Y; Xia, F; Yu, P, Graph Self-Supervised Learning: A Survey, IEEE Transactions on Knowledge and Data Engineering, 2022

Collections