Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce to drug and advanced material discovery. Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks. This survey aims to provide a comprehensive review of recent advances for improving the reliability of DGL algorithms against the above threats. In contrast to prior related surveys which mainly focus on adversarial attacks and defense, our survey covers more reliability-related aspects of DGL, i.e., inherent noise and distribution shift. Additionally, we discuss the relationships among above aspects and highlight some important issues to be explored in future research.

翻译：深度图学习（DGL）在金融、电子商务等商业领域以及药物和先进材料发现等科学领域均取得了显著进展。尽管成果斐然，但将DGL应用于实际场景仍面临一系列可靠性威胁，包括内在噪声、分布偏移和对抗攻击。本综述旨在全面梳理针对上述威胁提升DGL算法可靠性的最新研究进展。与先前主要聚焦对抗攻击与防御的相关综述不同，本文涵盖了DGL中更多可靠性相关方面，即内在噪声和分布偏移。此外，我们探讨了上述方面之间的关联，并指出了未来研究中值得探索的重要问题。