Juntae Kim

GCL-UP: Graph Contrastive Learning via Uncertainty-Aware Perturbation for Few-Shot Node Classification

Few-shot node classification is a fundamental challenge for Graph Neural Networks (GNNs), where models must generalize from a limited number of labeled nodes. Under such label scarcity, conventional studies have employed Graph Contrastive Learning with graph view augmentation to improve representation learning. However...