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arXiv Machine Learning By Mansooreh Pakravan

Uncertainty Quantification Is Indispensable for Reliable Connectome-Based Graph Learning: A Narrative Review and Case Study

Read the original on arXiv Machine Learning →

The paper reviews uncertainty quantification (UQ) methods for graph neural networks used in connectome-based diagnostic classification and presents a case study on a temporal Graph Attention Network applied to dynamic functional connectivity data for Cocaine Use Disorder. It highlights that deterministic GNNs can produce overconfident predictions, as shown by a Monte Carlo dropout audit revealing high confidence on misclassified subjects. The study demonstrates the need for rigorous UQ, calibration, and selective prediction to ensure reliable graph-based biomarkers in clinical neuroscience.

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