Graph Self-Supervised Learning: A Survey

• URL: http://arxiv.org/abs/2103.00111v1
• Date: Sat, 27 Feb 2021 03:04:21 GMT
• Title: Graph Self-Supervised Learning: A Survey
• Authors: Yixin Liu, Shirui Pan, Ming Jin, Chuan Zhou, Feng Xia, Philip S. Yu
• Abstract summary: Self-supervised learning (SSL) has become a promising and trending learning paradigm for graph data. We present a timely and comprehensive review of the existing approaches which employ SSL techniques for graph data.
• Score: 73.86209411547183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning on graphs has attracted significant interest 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 other domains like computer vision/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 divide these into four categories according to the design of their pretext tasks. We further discuss the remaining challenges and potential future directions in this research field.

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