Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion

arXiv:2604.27462v1 Announce Type: cross Abstract: Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention. Despite recent advances in graph few-shot learning that have demonstrated promising performance, existing methods still suffer from several key limitations. First, during the meta-training phase, these methods typically perform node representation learning in Euclidean