Dual-frequency Decoupling for Exploring Inherent Structural and Topological Features in Homogeneous Graphs

While graph transformer methods effectively enlarge the receptive field, the complexity of homogeneous graph data compromises global attention’s ability to capture local structural features. This limitation introduces task-irrelevant long-range topological information, resulting in task representation inaccuracies. To address this challenge, we propose a high- and low-frequency decoupled adaptive constraint method to explicitly capture structural and topological features. First, a dual-frequency decoupling mechanism is designed in the feature capture phase to refine long-range topological information and better distinguish features. Subsequently, residual attention gating augmented by long-range prior knowledge is introduced in the feature integration phase to ensure coherent integration. Finally, multi-scale consistency constraints are proposed in the representation phase to align the integrated representation with the mapping of long-range topological information, ensuring robust training. Our method was benchmarked on six homogeneous datasets, outperforming state-of-the-art methods by 2.15% and 2% on the Cora and Citeseer datasets, respectively.