5 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
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.…
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…
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…
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Yonghao Liu, Mengyu Li, Fausto Giunchiglia +4
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph model…
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Yonghao Liu, Fausto Giunchiglia, Ximing Li +3
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among rese…