系统梳理图学习中缺失数据问题，分类总结解决方案。
Incomplete Graph Learning: A Comprehensive Survey
- 按缺失类型分三类：属性缺失、节点缺失、混合缺失
- 归纳现有方法共性与差异，明确适用场景
- 提供数据集、评估指标与研究方向参考，适合入门者
图学习是处理普遍存在的图数据的重要领域。然而，现有方法对图中缺失属性敏感，导致结果不理想。为此，不完整图学习应运而生，旨在处理不完整图以获得更准确、更具代表性的结果。本文首次全面综述该领域，首先对不完整图进行分类并明确定义相关概念与技术；其次根据缺失类型将方法分为三类：（1）属性不完整图学习，（2）属性缺失图学习，（3）混合缺失图学习。通过系统分类与总结，揭示现有方法的共性与差异，帮助研究者选择合适方法。此外，整理了常用数据集、处理模式、评估指标及应用领域。最后，讨论当前挑战并提出未来方向，推动该领域发展。我们还建立了在线资源库（https://github.com/cherry-a11y/Incomplete-graph-learning.git），便于追踪最新进展。
原文摘要 · Abstract (English)
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.We developed an online resource to follow relevant research based on this review, available at https://github.com/cherry-a11y/Incomplete-graph-learning.git
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