Deep graph learning has achieved remarkable progresses in both business and
scientific areas ranging from finance and e-commerce, to drug and advanced
material discovery. Despite these progresses, how to ensure various deep graph
learning algorithms behave in a socially responsible manner and meet regulatory
compliance requirements becomes an emerging problem, especially in
risk-sensitive domains. Trustworthy graph learning (TwGL) aims to solve the
above problems from a technical viewpoint. In contrast to conventional graph
learning research which mainly cares about model performance, TwGL considers
various reliability and safety aspects of the graph learning framework
including but not limited to robustness, explainability, and privacy. In this
survey, we provide a comprehensive review of recent leading approaches in the
TwGL field from three dimensions, namely, reliability, explainability, and
privacy protection. We give a general categorization for existing work and
review typical work for each category. To give further insights for TwGL
research, we provide a unified view to inspect previous works and build the
connection between them. We also point out some important open problems
remaining to be solved in the future developments of TwGL.

综述了可靠性、可解释性和隐私保护等多个方面的可信图学习领域的最新进展和开放性问题。