取消掩码！图属性缺失机制的GAMM分类法
Drop the mask! GAMM-A Taxonomy for Graph Attributes Missing Mechanisms
AI总结：
本文提出GAMM分类法，用于分析图属性缺失机制，揭示传统填补方法在图感知缺失场景中的不足。
摘要：
在具有属性的图中探索缺失数据引入了超越表格数据集中发现的独特挑战。在本工作中，我们通过提出GAMM（图属性缺失机制），一种将缺失概率系统地与节点属性和底层图结构联系起来的框架，扩展了缺失数据机制的分类法到属性图。我们的分类法通过引入图特定的依赖关系丰富了传统掩码机制的定义。我们实证地表明，尽管最先进的填补方法在传统掩码上有效，但当面对这些更现实的图感知缺失场景时，它们显著挣扎。
英文摘要：
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.
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。
