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

ArXi:2601.04855v2 Announce Type: replace Handling missing node features is a key challenge for deploying Graph Neural Networks (GNNs) in real-world domains such as healthcare and sensor networks. Existing studies mostly address relatively benign scenarios, namely benchmark datasets with (a) high-dimensional but sparse node features and (b) incomplete data generated under Missing Completely At Random (MCAR) mechanisms.