提出图属性缺失机制新分类，揭示现有方法在真实场景下失效原因
Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanisms
- 构建GAMM框架，将缺失概率与节点属性和图结构关联
- 实验证明主流填补方法在真实缺失场景下性能大幅下降
- 适合研究图数据缺失问题或改进填补算法的研究者
属性图中的缺失数据研究带来了超越表格数据集的独特挑战。本文通过提出GAMM（Graph Attributes Missing Mechanisms）框架，将缺失概率系统性地关联到节点属性和底层图结构，扩展了传统缺失机制分类。该分类通过引入图特异性依赖关系，丰富了经典缺失机制定义。实验表明，尽管当前最先进的填补方法在传统掩码下表现良好，但在更贴近现实的图感知缺失场景中表现显著恶化。
原文摘要 · Abstract (English)
Exploring missing data in attributed graphs introduces unique challenges beyond those found in tabular datasets. In this work, we extend the taxonomy for missing data mechanisms to attributed graphs by proposing GAMM (Graph Attributes Missing Mechanisms), a framework that systematically links missingness probability to both node attributes and the underlying graph structure. Our taxonomy enriches the conventional definitions of masking mechanisms by introducing graph-specific dependencies. We empirically demonstrate that state-of-the-art imputation methods, while effective on traditional masks, significantly struggle when confronted with these more realistic graph-aware missingness scenarios.
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