Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.

翻译：图神经网络（GNNs）近年来发展迅速。由于其强大的图结构数据建模能力，GNNs被广泛应用于各类场景，包括金融分析、交通预测和药物发现等高风险评估领域。尽管GNN在现实世界中展现出造福人类的巨大潜力，但近期研究表明，GNN会泄露隐私信息、易受对抗攻击、可能继承并放大训练数据中的社会偏见，且缺乏可解释性，这可能导致对用户和社会造成无意的伤害。例如，现有研究证明，攻击者可通过在训练图上施加难以察觉的扰动来欺骗GNN，使其输出攻击者期望的结果。基于社交网络训练的GNN可能在其决策过程中嵌入歧视，从而强化不良社会偏见。因此，面向隐私、鲁棒性、公平性和可解释性等不同维度的可信GNN研究应运而生，旨在防范GNN模型带来的危害并增强用户对GNN的信任。本文从计算视角对GNN在隐私、鲁棒性、公平性和可解释性四个方面进行了全面综述。针对每个方面，我们给出了相关方法的分类体系，并为多类可信GNN构建了通用框架。此外，我们还探讨了每个方面的未来研究方向以及这些方面之间的关联，以助力实现可信性。