CPT: Competence-progressive Training Strategy for Few-shot Node Classification

Graph Neural Networks (GNNs) have made significant advancements in node classification, but their success relies on sufficient labeled nodes per class in the training data. Real-world graph data often exhibits a long-tail distribution with sparse labels, emphasizing the importance of GNNs' ability in few-shot node classification, which entails categorizing nodes with limited data. Traditional episodic meta-learning approaches have shown promise in this domain, but they face an inherent limitation: it might lead the model to converge to suboptimal solutions because …

class classification cs.lg data distribution few-shot gnns graph graph data graph neural networks importance labels networks neural networks node per strategy success training training data world