Before revision:  However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, and overfitting. To address these issues , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand).
After revision:  However, existing GNNs often meet complex graph structures with scarce labeled nodes and suffer from the limitations of non-robustness, over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper , we propose a simple yet effective GNN framework---Graph Random Neural Network (Grand).