结构-特征协同防御的图神经网络OA

To address the degradation of node representations in graph neural networks with complex perturbation environments,a structure-feature collaborative defense graph neural network named SFCoRobustGNN was pro-posed.Structurally,a sparse attention mechanism that integrated structure priors to dynamically suppress anomalous edges was introduced.Feature-wise,a channel gating mechanism was combined with a nonlinear feature mixing module(FeatureMixPro)to enhance the model's adaptability to feature perturbations.A collaborative dual-pathway defense was achieved through adversarial training and a multi-objective optimization strategy.Experiments on multi-ple benchmark datasets,including Cora and Citeseer,demonstrated that the proposed method outperformed most of mainstream baseline metods with various intensities of structure perturbations(5%—40%)and feature attacks(ε=0.01-0.10),showing significant improvement in node classification accuracy.On the large-scale ogbn-products dataset,it maintained an accuracy of 71.82%even with a 20%MetaAttack structure perturbation,demonstrating its strong scalability.Ablation studies validated the effectiveness and synergistic effects of each module.The pro-posed method effectively mitigated performance degradation with complex perturbations and exhibited excellent gen-eralization.