Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. Particularly, in this paper, we refer to the task of classifying nodes in classes with a few labeled nodes as the few-shot node classification problem. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes via a novel similarity-sensitive mix-up strategy. Extensive experiments on few-shot node classification datasets verify the superiority of our framework over state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/COSMIC.

翻译：少样本节点分类旨在仅利用有限标记节点作为参考，为图上的节点预测标签，这在现实图挖掘任务中具有重要意义。具体而言，本文中将基于少量标记节点对类别节点进行分类的任务定义为少样本节点分类问题。为应对这种标记短缺问题，现有研究通常采用元学习框架，通过大量片段从具有丰富标记节点的类别中提取可迁移知识，并将这些知识泛化到其他标记有限的类别中。本质上，少样本节点分类的核心目标是学习能够跨不同类别泛化的节点嵌入。为实现这一目标，图神经网络编码器必须既能区分不同类别的节点嵌入，又能对齐同一类别内的节点嵌入。因此，本研究提出同时考虑模型的类内与类间泛化能力。我们设计了一种创新的图对比元学习框架COSMIC，包含两个关键设计：首先，通过在每个片段中引入对比两步优化机制，显式对齐同类节点嵌入，以增强类内泛化能力；其次，通过新型相似度敏感的混合策略生成硬节点类别，以强化类间泛化能力。在少样本节点分类数据集上的大量实验验证了我们的框架相较于现有最优基线方法的优越性。我们的代码已开源至https://github.com/SongW-SW/COSMIC。