Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications such as 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, such as 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 characterized 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 summarize existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. In addition, 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 industrialization of trustworthy GNNs.

中文翻译：

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值得信赖的图神经网络：方面、方法和趋势

图神经网络（GNN）已经成为一系列适用于各种现实场景的有效图学习方法，从推荐系统和问答等日常应用到生命科学中的药物发现和 n-body 等尖端技术天体物理学中的模拟。然而，任务性能并不是 GNN 的唯一要求。面向性能的 GNN 表现出了潜在的不利影响，例如容易受到对抗性攻击、对弱势群体无法解释的歧视或边缘计算环境中的过度资源消耗。为了避免这些无意的伤害，有必要建立以可信为特征的、有能力的 GNN。为此，我们提出了一个全面的路线图，从涉及的各种计算技术的角度构建值得信赖的 GNN。在本次调查中，我们介绍了基本概念，并从鲁棒性、可解释性、隐私性、公平性、问责性和环境福祉六个方面全面总结了可信 GNN 的现有努力。此外，我们强调了可信 GNN 的上述六个方面之间错综复杂的跨方面关系。最后，我们全面概述了促进可信赖 GNN 的研究和产业化的趋势方向。