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Stress-Testing Structure-Aware Calibration of Malware Graph Neural Networks under Type Shift
Post-hoc malware calibrators can condition confidence on graph structure, but their structural inputs may leave the support represented by validation data under malware-type shift. We study this risk on MalNet-Tiny by holding out each of four malware types across three seeds, freezing a graph isomor
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