arXiv cs.AI· Enrico Chial\`a, Francesco Ferrini, Antonio Longa, Bruno Lepri, Filippo Maria Bianchi, Manfred Jaeger, Veronica Lachi·· 3 天前AI 评分22
当缺失性沿图传播：PEMix 超越特征传播的缺失特征处理方法
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
AI 导读
针对图神经网络中缺失特征沿图边传播的现实场景，研究者形式化了"图结构掩码依赖"这一概念，并发现基于传播的现有方法在该设定下效果与均匀随机缺失时差异显著。为此提出 PEMix，不再传播观测值，而是用全局结构表示对不完整节点特征的概率模型进行条件化，再执行消息传递。在受控图结构缺失与自然缺失实验中，PEMix 在复杂缺失机制下保持稳健，缺失越严重表现越突出。
正文 · 原文
Abstract:Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications. By leveraging the dependence between node features and graph structure, existing graph-specific methods handle missingness by propagating observed feature values along graph edges. However, these methods have mainly been evaluated in overly optimistic settings, where feature entries are removed independently and uniformly across the graph. This overlooks realistic scenarios in which missingness propagates among connected nodes, as can occur during localized sensing failures in sensor networks. We formalize this complementary notion as graph-structured mask dependence, where the joint distribution of missingness indicators is shaped by the graph, and introduce a controlled mechanism that instantiates this setting. We study propagation-based methods theoretically and empirically in this setting, showing that their effectiveness changes substantially compared with the uniformly random regime. Motivated by this limitation, we propose PEMix, designed to remain effective under challenging graph-structured missingness. Rather than propagating observed values, PEMix uses global structural representations to condition a probabilistic model of incomplete node features before message passing. Experiments under controlled graph-structured missingness and naturally occurring missingness show that PEMix remains robust across complex missingness regimes, with particularly strong performance as missingness becomes severe.
| Comments: | 29 pages, 3 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18113 [cs.LG] |
| (or arXiv:2610.18113v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18113 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Antonio Longa [view email]
[v1]
Tue, 13 Oct 2026 10:25:03 UTC (297 KB)
来源：arXiv cs.AI · arxiv.org