MDFS-GNN: Multi-Dilation Graph Propagation with Feature Shrinkage for Semi-Supervised Node Classification

Graph neural networks (GNNs) have been widely used for semi-supervised node classification by propagating structural and feature information across graph neighborhoods. However, when only a limited number of labeled nodes are available, insufficient supervision restricts effective information propagation between labeled and unlabeled nodes. Although high-order GNNs can enlarge the receptive field to capture long-range structural information, their insufficient modeling of local information and ineffective integration of local and global features may limit the learning of discriminative node representations. To address these challenges, we propose MDFS-GNN, a Multi-Dilation Graph Propagation with Feature Shrinkage framework for semi-supervised node classification. Specifically, MDFS-GNN employs graph convolution operations with different dilation factors to capture multi-order neighborhood information and incorporates the original node attributes through residual aggregation to preserve informative features during propagation. Furthermore, a feature shrinkage mechanism reconstructs the neighborhood structure according to node feature similarity and retains the Top-$K$ most similar nodes, thereby strengthening informative local interactions while reducing redundant information. These components are integrated into a two-stage learning framework consisting of initialization and iterative refinement, in which node representations and pseudo-labels are progressively updated to facilitate information propagation across the graph. Experiments on three homophilic and four heterophilic graph datasets under different labeling rates demonstrate the effectiveness and robustness of MDFS-GNN, particularly in scenarios with extremely limited labeled nodes.