用上下文学习让图模型少样本分类更高效，无需微调。
Advancing Graph Few-Shot Learning via In-Context Learning
- 将图少样本学习转为无微调的序列推理，利用上下文动态生成类别表示。
- 在多个基准数据集上超越现有方法，支持集仅需5个样本即达高精度。
- 适合需要快速部署、标签稀缺的图数据场景，如新用户分类或异常检测。
图少样本学习旨在仅用少量标注样本对新类别的节点进行分类，是图学习中的重要问题。然而，现有方法存在两大局限：一是主流范式依赖监督任务，无法利用图中大量未标记节点；二是多数方法在推理时需复杂任务适配或微调，影响效率与适用性。受大语言模型上下文学习能力启发，我们提出VISION模型——一种通过上下文学习推进图少样本学习的新方法。该模型将图少样本学习重构为免微调的序列推理问题。核心是一个上下文感知网络，通过角色嵌入初始化节点，并采用双上下文融合模块，协同整合局部拓扑结构与全局任务级依赖关系。这使得模型能在单次前向传播中，根据支持集上下文动态生成查询集的类别感知表示。为有效训练，我们引入无监督任务生成器，从海量未标记数据中构建结构自适应特征与多样化伪任务。该方法统一了无监督元学习与图上下文学习，实现高效推理。在多个基准数据集上的实验表明其优越性。公开代码已发布。
原文摘要 · Abstract (English)
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. Second, many approaches require complex task adaptation or fine-tuning during inference, limiting their efficiency and applicability. Inspired by the powerful in-context learning capabilities of large language models, we propose a novel model named VISION for adVancIng graph few-Shot learning via In-cOntext LearNing to address these challenges. Our model reframes graph few-shot learning as a fine-tuning-free sequence reasoning problem. At its core is a context-aware network that initializes nodes with role embeddings and employs a dual-context fusion module to synergistically integrate local topological structures and global task-level dependencies. This allows our model to dynamically generate class-aware representations for the query set conditioned on the support set context in a single forward pass. To effectively train our model, we introduce an unsupervised task generator that creates structure-adaptive features and constructs diverse pseudo-tasks from abundant unlabeled data. Our method unifies unsupervised meta-learning with graph in-context learning, achieving efficient inference. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our model. Our public code can be found
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