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HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration
For graph node classification, calibrated class probabilities are needed when confidence scores, usually the maximum predicted class probability, are used to rank predictions, defer uncertain nodes to human review, or control risk. Existing post-hoc calibrators either apply one global temperature or
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