通过归一化增强节点分类泛化能力，少样本下表现更优
Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification
- 归一化节点表示并解耦方向与长度，提升信息聚合一致性
- 在低标签率下达到当前最佳性能，计算开销低
- 适合标签稀缺场景的图神经网络优化，如医疗、社交网络
图神经网络在半监督节点分类中表现优异，但多数方法严重依赖大量标注数据，成本高且需专业知识。本文从少样本半监督学习视角分析了监督信号对GNN泛化能力的限制。提出新算法NormProp，利用未标记节点的同质性假设生成额外监督信号，以增强对标签稀缺的鲁棒性。核心思想是通过解耦节点表示的方向与欧氏范数，高效捕捉类别信息和消息传递过程中的聚合一致性。进一步进行理论分析，推导出欧氏范数上界，并提出同质性正则化约束未标记节点的一致性。大量实验表明，NormProp在低标签率条件下实现最优性能，且计算复杂度低。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data for training, which is labor-intensive and requires extensive domain knowledge. In this paper, we first analyze the restrictions of GNNs generalization from the perspective of supervision signals in the context of few-shot semi-supervised node classification. To address these challenges, we propose a novel algorithm named NormProp, which utilizes the homophily assumption of unlabeled nodes to generate additional supervision signals, thereby enhancing the generalization against label scarcity. The key idea is to efficiently capture both the class information and the consistency of aggregation during message passing, via decoupling the direction and Euclidean norm of node representations. Moreover, we conduct a theoretical analysis to determine the upper bound of Euclidean norm, and then propose homophilous regularization to constraint the consistency of unlabeled nodes. Extensive experiments demonstrate that NormProp achieve state-of-the-art performance under low-label rate scenarios with low computational complexity.
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