Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks
Abstract
Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncertainty quantification (UQ), limiting their usefulness in high-stakes scenarios. We propose conformalized SNN (CF-SNN), a framework that, given a simplicial-complex entity with m prediction targets, generates prediction sets for all m targets simultaneously. Under mild assumptions, CF-SNN guarantees that, for each target, the prediction set contains the true label with a user-specified nominal coverage probability, and the realized miscoverage proportion across all m targets is guaranteed to remain close to the nominal level with high probability. Extensive experiments across a wide range of SC prediction tasks demonstrate that CF-SNN is a flexible UQ framework that achieves user-specified coverage while producing informative prediction sets of small size.
