  Node classification is an essential problem in graph learning. However, many
models typically obtain unsatisfactory performance when applied to few-shot
scenarios. Some studies have attempted to combine meta-learning with graph
neural networks to solve few-shot node classification on graphs. Despite their
promising performance, some limitations remain. First, they employ the node
encoding mechanism of homophilic graphs to learn node embeddings, even in
heterophilic graphs. Second, existing models based on meta-learning ignore the
interference of randomness in the learning process. Third, they are trained
using only limited labeled nodes within the specific task, without explicitly
utilizing numerous unlabeled nodes. Finally, they treat almost all sampled
tasks equally without customizing them for their uniqueness. To address these
issues, we propose a novel framework for few-shot node classification called
Meta-GPS++. Specifically, we first adopt an efficient method to learn
discriminative node representations on homophilic and heterophilic graphs.
Then, we leverage a prototype-based approach to initialize parameters and
contrastive learning for regularizing the distribution of node embeddings.
Moreover, we apply self-training to extract valuable information from unlabeled
nodes. Additionally, we adopt S$^2$ (scaling & shifting) transformation to
learn transferable knowledge from diverse tasks. The results on real-world
datasets show the superiority of Meta-GPS++. Our code is available here.
