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.

综述研究了图学习中的分布转变问题及解决方法，将现有的图学习方法分为图领域适应学习、图越域学习和图持续学习三种关键场景，详细讨论了这些场景下的现有进展，并对该领域进行了潜在应用和未来研究方向的分析。