Graph Random Neural Network for Semi-Supervised Learning on Graphs

TL;DR

This paper introduces GRAND, a novel semi-supervised learning framework for graphs that uses random data augmentation and consistency regularization to improve robustness, generalization, and performance over existing GNNs.

Contribution

GRAND is a new framework that combines random graph data augmentation with consistency regularization to enhance semi-supervised learning on graphs.

Findings

Outperforms state-of-the-art GNNs on benchmark datasets

Mitigates over-smoothing and non-robustness issues

Exhibits better generalization in semi-supervised node classification

Abstract

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS (GRAND) -- to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https://github.com/Grand20/grand.