Graph Random Neural Network for Semi-Supervised Learning on Graphs

Understand

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.