Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area.

翻译：图学习因其在建模图结构数据所代表的复杂数据关系方面的有效性，在从社交网络分析到推荐系统的多种应用场景中发挥着关键作用，并获得了广泛关注。然而，现实中的图数据通常随时间动态变化，节点属性和边结构不断改变，导致严重的图数据分布偏移问题。分布偏移的多样性和复杂性进一步加剧了这一挑战，会显著损害图学习方法的泛化与适应能力，对其有效性构成严峻考验。在本综述中，我们全面回顾并总结了在图学习背景下应对分布偏移的最新方法、策略与见解。具体而言，根据推理阶段分布的可观测性以及训练阶段充足监督信息的可用性，我们将现有图学习方法划分为几种关键场景，包括图域适应学习、图分布外学习与图持续学习。针对每种场景，我们提出了详细的分类体系，并对分布偏移图学习中的现有进展进行了具体描述与讨论。此外，我们结合对该领域当前现状的系统分析，探讨了分布偏移下图学习的潜在应用与未来方向。本综述旨在为开发有效的图学习算法以处理图分布偏移提供一般性指导，并推动该领域的未来研究与进展。