VISION Model Advances Graph Few-Shot Learning with In-Context Learning
A new model named VISION (adVancIng graph few-Shot learning via In-cOntext LearNing) has been proposed to address key limitations in graph few-shot learning. The approach reframes the problem as a fine-tuning-free sequence reasoning task, inspired by in-context learning in large language models. VISION uses a context-aware network with role embeddings and a dual-context fusion module to integrate unlabeled nodes without complex task adaptation. The research is detailed in arXiv preprint 2605.24410.
Key facts
- VISION is a model for graph few-shot learning
- It uses in-context learning inspired by large language models
- The approach is fine-tuning-free
- It leverages unlabeled nodes in graphs
- The model uses role embeddings and a dual-context fusion module
- The research is published on arXiv with ID 2605.24410
- Graph few-shot learning aims to classify nodes from novel classes with few labeled examples
- Existing methods often rely on supervised tasks and require complex adaptation
Entities
Institutions
- arXiv