Pattern-Aware Graph Neural Networks for Handling Missing Data

Missing data is ubiquitous in real-world datasets. Traditional methods either discard incomplete samples or apply imputation techniques that ignore potentially informative missingness patterns, implicitly assuming that missingness occurs randomly. However, missingness patterns might provide additional information. We propose pattern-aware graph neural networks that explicitly encode which features are missing alongside observed values. We used four encoding strategies-learned embeddings, frozen random embeddings, statistical features, and hierarchical representations-across seven UCI datasets with naturally occurring missingness. Our Pattern-aware methods achieve substantial improvements over baselines, with an average improvement of $17 \%$ in balanced accuracy and $22 \%$ in F1-macro across all datasets. Our code and data are available at https://github.com/TranMinett/pattern_aware_GRAPE.