F. D. Nijs

Constraint-Aware Self-Supervised Learning for Edge Selection

A reusable self-supervised framework for edge-selection optimization that learns directly from unlabeled instances is proposed, and a lightweight graph architecture centered on a cost-attention convolution is introduced, where edge costs and feasibility information directly shape message passing.