When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features
Graph Neural Networks (GNNs) commonly assume that node features are fully observed, despite missing features being pervasive in real-world applications.
ProofPaper ↗
Key points
- By leveraging the dependence between node features and graph structure, existing graph-specific methods handle missingness by propagating observed feature values along graph edges.
- 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.
- Motivated by this limitation, we propose PEMix, designed to remain effective under challenging graph-structured missingness.
- 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.
Sources (1)
- [1]When Missingness Follows the Graph: Beyond Feature Propagation for Missing FeaturesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 10:25 AM
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.
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