# Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification

arXiv: https://arxiv.org/abs/2501.08581 · HTML: https://mlsanity.com/paper/2501.08581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering](https://mlsanity.com/paper/2511.19976/) (2025)
- [Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification](https://mlsanity.com/paper/2605.20248/) (2026)

## Abstract (arXiv)

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.