Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success in these areas. However, in real-world scenarios, the training environment for models is often far from ideal, leading to substantial performance degradation of GNN models due to various unfavorable factors, including imbalance in data distribution, the presence of noise in erroneous data, privacy protection of sensitive information, and generalization capability for out-of-distribution (OOD) scenarios. To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness. In this paper, we present a comprehensive survey that systematically reviews existing GNN models, focusing on solutions to the four mentioned real-world challenges including imbalance, noise, privacy, and OOD in practical scenarios that many existing reviews have not considered. Specifically, we first highlight the four key challenges faced by existing GNNs, paving the way for our exploration of real-world GNN models. Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. Last but not least, we outline promising directions and offer future perspectives in the field.

翻译：图结构数据在社交网络分析、生物化学、金融欺诈检测及网络安全等众多领域展现出普遍性和广泛适用性。利用图神经网络在这些领域已取得显著进展。然而，在真实场景中，模型训练环境常远非理想，数据分布不平衡、含噪错误数据、敏感信息隐私保护以及对分布外场景的泛化能力等不利因素，导致图神经网络模型性能大幅下降。为解决这些问题，大量研究工作致力于提升图神经网络模型在真实场景中的性能及其可靠性与鲁棒性。本文对现有图神经网络模型进行了系统综述，重点关注现有综述尚未充分考虑的四个真实世界挑战——不平衡、噪声、隐私和分布外——的解决方案。具体而言，我们首先指出当前图神经网络面临的四大关键挑战，为探索真实世界图神经网络模型奠定基础。随后，我们从这四个方面展开详细讨论，剖析各类解决方案如何提升图神经网络模型的可靠性与鲁棒性。最后，我们概述了该领域的潜在研究方向并展望了未来发展前景。