Abstract: Graph data augmentation enhances the generalization and robustness of graph neural networks(GNNs) by performing local or global transformations on graph structures or node features.While existing studies have shown that graph augmentation techniques can effectively leverage low-frequency information to capture the global topology of graphs,they often fail to preserve high-frequency components that encode fine-grained structural details.This shortcoming may result in information loss or feature distortion when learning local representations.To address this challenge,this paper proposes a phase-preserving frequency-domain MinMax framework for graph augmentation.The proposed method integrates frequency-domain analysis with the MinMax optimization paradigm,decomposing graph signals into low and high-frequency components.The low-frequency part captures global topological patterns,whereas the high-frequency part represents rich local structural information.By applying the MinMax strategy in the frequency domain,the proposed framework simultaneously preserves global structure and enhances high-frequency details,leading to more expressive multi-scale graph representations.In addition,it adopts an adaptive augmentation strategy that dynamically adjusts the perturbation amplitude based on the characteristics of different frequency components,thereby improving training efficiency.The phase information,which encodes intrinsic structural relations between graph nodes,is explicitly preserved to further enrich the expressive capacity of node representations.Through this frequency-aware design,the proposed method maintains essential topological structures while effectively enhancing node-level features,improving the GNN’s ability to capture both global and local semantics.Extensive experiments on multiple benchmark datasets demonstrate that the proposed method achieves over a 2 percentage points accuracy gain on node classification tasks compared to existing approaches.Moreover,it deli-vers superior computational efficiency,validating its effectiveness and scalability for large-scale graph learning scenarios.

Key words: Graph augmentation, Phase preservation, Frequency domain MinMax framework, Topological optimization, Amplitude adjustment