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This paper proposes RHEA, a reliability-aware framework for multimodal-attributed graph clustering that estimates node-specific modality reliability from neighborhood consensus, reconstructs unreliable modalities, and uses reliability-aware fusion and optimal transport clustering. Experiments on four benchmarks show consistent gains, especially under noisy or missing attributes.
This paper introduces SEB-Cal, a method that augments output-space calibration with spectral features (band energy, entropy, peak dominance, phase stability) to improve selective reliability estimation in time-series classification, achieving higher Corr-AUROC and lower [email protected] across multiple datasets.