  Graph neural networks (GNNs) have emerged as a series of competent graph
learning methods for diverse real-world scenarios, ranging from daily
applications like recommendation systems and question answering to cutting-edge
technologies such as drug discovery in life sciences and n-body simulation in
astrophysics. However, task performance is not the only requirement for GNNs.
Performance-oriented GNNs have exhibited potential adverse effects like
vulnerability to adversarial attacks, unexplainable discrimination against
disadvantaged groups, or excessive resource consumption in edge computing
environments. To avoid these unintentional harms, it is necessary to build
competent GNNs characterised by trustworthiness. To this end, we propose a
comprehensive roadmap to build trustworthy GNNs from the view of the various
computing technologies involved. In this survey, we introduce basic concepts
and comprehensively summarise existing efforts for trustworthy GNNs from six
aspects, including robustness, explainability, privacy, fairness,
accountability, and environmental well-being. Additionally, we highlight the
intricate cross-aspect relations between the above six aspects of trustworthy
GNNs. Finally, we present a thorough overview of trending directions for
facilitating the research and industrialisation of trustworthy GNNs.
