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cs.LG2026
Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
Yonghao Liu, Jialu Sun, Wei Pang +4
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.…
cs.LG2025
Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs
Renchu Guan, Xuyang Li, Yachao Zhang +5
Hypergraphs, as a generalization of traditional graphs, naturally capture high-order relationships. In recent years, hypergraph neural networks (HNNs) have been widely used to capt…
cs.LG2025
Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration
Yonghao Liu, Yajun Wang, Chunli Guo +5
Graph few-shot learning has attracted increasing attention due to its ability to rapidly adapt models to new tasks with only limited labeled nodes. Despite the remarkable progress…