用图模型处理异构数据缺失，效果优于传统方法。
IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
- 构建双解码器图自编码框架，分别重建特征与缺失模式。
- 在16个数据集上30%缺失率下RMSE和F1均提升，覆盖三类缺失场景。
- 适合处理含数值/分类变量的复杂真实数据，尤其缺失机制不明时。
真实世界表格数据中的缺失问题仍具挑战性，尤其当数据包含数值与分类特征时。现有填补方法难以捕捉复杂结构依赖且对异构数据处理不佳。本文提出IVGAE，一种变分图自编码框架，用于鲁棒填补不完整异构数据。IVGAE通过二分图表示样本-特征关系，并利用图表示学习建模结构依赖。关键创新在于双解码器架构：一个解码器重建特征嵌入，另一个建模缺失模式，提供对缺失机制敏感的结构先验。为更好编码分类变量，引入基于Transformer的异构嵌入模块，避免高维独热编码。在16个真实数据集上的实验表明，当缺失率达30%时，无论随机缺失（MCAR）、条件缺失（MAR）还是非随机缺失（MNAR），IVGAE在均方根误差（RMSE）与下游分类F1上均有稳定提升。代码与数据见：https://github.com/echoid/IVGAE。
原文摘要 · Abstract (English)
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing imputation methods often fail to capture complex structural dependencies and handle heterogeneous data effectively. We present \textbf{IVGAE}, a Variational Graph Autoencoder framework for robust imputation of incomplete heterogeneous data. IVGAE constructs a bipartite graph to represent sample-feature relationships and applies graph representation learning to model structural dependencies. A key innovation is its \textit{dual-decoder architecture}, where one decoder reconstructs feature embeddings and the other models missingness patterns, providing structural priors aware of missing mechanisms. To better encode categorical variables, we introduce a Transformer-based heterogeneous embedding module that avoids high-dimensional one-hot encoding. Extensive experiments on 16 real-world datasets show that IVGAE achieves consistent improvements in RMSE and downstream F1 across MCAR, MAR, and MNAR missing scenarios under 30\% missing rates. Code and data are available at: https://github.com/echoid/IVGAE.
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