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Deep graph learning (DGL) has achieved remarkable progress in both businessand scientific areas ranging from finance and e-commerce to drug and advancedmaterial discovery. Despite the progress, applying DGL to real-worldapplications faces a series of reliability threats including inherent noise,distribution shift, and adversarial attacks. This survey aims to provide acomprehensive review of recent advances for improving the reliability of DGLalgorithms against the above threats. In contrast to prior related surveyswhich mainly focus on adversarial attacks and defense, our survey covers morereliability-related aspects of DGL, i.e., inherent noise and distributionshift. Additionally, we discuss the relationships among above aspects andhighlight some important issues to be explored in future research.

Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack