Advancing Graph Few-Shot Learning via In-Context Learning
The article discusses advancements in graph few-shot learning through a novel model called VISION. This model addresses limitations of existing methods by reframing the learning process and utilizing in-context learning. Extensive experiments demonstrate the model's superiority over traditional approaches.
- ▪Graph few-shot learning aims to classify nodes from novel classes with limited labeled examples.
- ▪Existing methods often rely on supervised tasks and require complex adaptations during inference.
- ▪The VISION model integrates local topological structures and global task-level dependencies for efficient inference.
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Computer Science > Artificial Intelligence arXiv:2605.24410 (cs) [Submitted on 23 May 2026] Title:Advancing Graph Few-Shot Learning via In-Context Learning Authors:Renchu Guan, Yajun Wang, Chunli Guo, Bowen Cao, Fausto Giunchiglia, Wei Pang, Yonghao Liu, Xiaoyue Feng View a PDF of the paper titled Advancing Graph Few-Shot Learning via In-Context Learning, by Renchu Guan and 7 other authors View PDF HTML (experimental) Abstract:Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods often face two key limitations. First, the predominant graph few-shot learning paradigm relies on supervised tasks, failing to leverage the vast number of unlabeled nodes in the graph.
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