Topology-aware node dropping augmentation for graph classification

Graph augmentations effectively enhance the robustness and generalization of Graph Neural Networks (GNNs), particularly for graph classification tasks. However, existing augmentation methods, like NodeDrop, randomly drop a certain portion of nodes to generate augmented graphs without preserving the essential topological structures of the original graph, potentially modifying label information. To address this issue, we introduce a novel Node-Dropping Augmentation (NDAUG) method for graph classification tasks. Our method leverages node degree as a criterion to selectively drop less important nodes (low-degree) and preserve essential graph structures, generating diverse and informative augmented graphs. Further, in the case of isolated nodes, we develop a structure learning method to reconnect these isolated nodes by learning attention-based relationships between nodes. Experiments demonstrate that combining the proposed NDAUG with existing GNN models yields an average improvement of 2–5% accuracy on eight graph classification benchmarks compared to the state-of-the-art baselines.