CSA-PTR: Context-Aware Feature Splitting and Polarized Topology Refinement for Reliable Selective Propagation in Graph Neural Networks

Graph Neural Networks (GNNs) have achieved strong performance on graph-structured data via neighborhood message passing. Recent studies on GNNs suggest that not all feature dimensions benefit equally from message passing, motivating preference-guided feature splitting rather than uniform aggregation. Empirically, the splitting criterion is affected by class-boundary nodes with label-inconsistent neighborhoods, which confound the estimation of which dimensions should be propagated. Moreover, conducting propagation on the original topology may amplify feature–topology mismatch, causing messages to be passed along incompatible edges. To address these issues, we propose a plug-and-play architecture called Core–Shell Adaptive augmentation with dual-branch Polarized Topology Refinement (CSA-PTR), through simultaneous consideration of clearer feature splitting and a more ideal topology for GNNs to better satisfy the selective propagation criterion. Specifically, CSA-PTR consists of three modules. Core–shell adaptive augmentation stabilizes node representations by a purity-aware clustering algorithm, which reduces the ambiguity in feature-preference estimation. Then, graph feature splitting allocates feature dimensions into a propagation branch and a feature-only branch based on learned preferences. Finally, Dual-branch Polarized Topology Refinement exploits these branches as complementary views to learn polarized weights, yielding a more desirable topology and improving information flow. Extensive experiments on diverse benchmarks show that CSA-PTR achieves competitive performance across the evaluated settings, while consistently improving several representative GNN backbones.