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Conference Aug 2026

AdaCal-GNN: Adaptive Confidence Calibration for Graph Neural Networks

Graph Neural Networks (GNNs) often produce poorly calibrated predictions, where the predicted probabilities do not accurately reflect the true likelihood of correctness. Recent studies indicate that miscalibration in GNNs exhibits a heterogeneous pattern: while neighborhood aggregation induces an overall underconfidenc...

Ji-Wei Li, Hao Yang, Li-Zhen Wu et al. · 0 citations
Sep 2026

Toward Improving Stochastic Neural Network Robustness via Arbitrary Distribution Injection.

A novel SNN named arbitrary distribution injection is proposed, which enables distribution modeling from nonpredefined, data-dependent distributions and introduces a conditional stochastic feature mapping mechanism to model feature distributions, together with a theoretically grounded variance-regularization loss.

Rui Zhou, Hao Yang, Wen-Xu Wang et al. · 0 citations

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