分布偏移下的图学习：关于域适应、分布外与持续学习的全面综述
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
AI总结：
该综述针对真实图数据动态变化引发的分布偏移问题，按推理分布可观测性与训练监督可得性将图学习方法分为图域适应、分布外、持续学习三类并梳理进展，同时探讨应用与未来方向，为相关算法开发提供指导。
摘要：
图学习凭借其在建模由图结构数据表示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各类应用场景中发挥着关键作用并受到广泛关注。现实中，真实世界的图数据通常会随时间呈现动态变化，节点属性与边结构不断改变，进而引发严重的图数据分布偏移问题。分布偏移本身具有多样且复杂的特性，这会进一步加剧该问题，大幅降低图学习方法的泛化能力与适应能力，对其有效性构成重大挑战。\n本综述针对图学习场景下应对分布偏移的最新方法、策略与见解展开全面回顾与总结。具体而言，我们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获得性，将现有图学习方法划分为若干核心场景，包括图域适应学习、图分布外学习以及图持续学习。针对每个场景，本文提出了详细的分类体系，并对分布偏移下的图学习领域现有研究进展进行了具体描述与讨论。此外，我们还系统分析了该领域的当前现状，探讨了分布偏移下图学习的潜在应用与未来方向。本综述旨在为开发应对图分布偏移的高效图学习算法提供通用指导，并推动该领域未来的研究与进步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
