  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 of random and uniform task assignment,
ignoring task difficulty levels. This could lead the meta-learner to face
complex tasks too soon, hindering proper learning. Ideally, the meta-learner
should start with simple concepts and advance to more complex ones, like human
learning. So, we introduce CPT, a novel two-stage curriculum learning method
that aligns task difficulty with the meta-learner's progressive competence,
enhancing overall performance. Specifically, in CPT's initial stage, the focus
is on simpler tasks, fostering foundational skills for engaging with complex
tasks later. Importantly, the second stage dynamically adjusts task difficulty
based on the meta-learner's growing competence, aiming for optimal knowledge
acquisition. Extensive experiments on popular node classification datasets
demonstrate significant improvements of our strategy over existing methods.
