Semantic Guide for Semi-Supervised Few-Shot Multi-Label Node Classification

Abstract

This work addresses few-shot multi-label node classification with limited labelled examples by incorporating semantic class descriptions as guidance signals. The semantic guide helps the model generalise across label combinations not seen during training, improving performance in low-resource graph learning settings.