Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution

In three linesarXiv paper on handling missing node features in Graph Neural Networks (GNNs). Authors prove existing benchmarks with sparse features limit meaningful performance comparison. They introduce GNNmim, a robust baseline evaluated on dense datasets with realistic missingness mechanisms beyond MCAR.

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