GLADC: Global Linear Attention and Dual Constraint for Mitigating Over-Smoothing in Graph Neural Networks

Graph Convolutional Networks (GCNs) enhance node representations by aggregating information from neighboring nodes, but deeper layers often suffer from over-smoothing, where node embeddings become indistinguishable. Transformers enable global dependency modeling on graphs but suffer from high computational costs and can exacerbate over-smoothing when multiple attention layers are stacked. To address these issues, we propose GLADC, a novel framework designed for semi-supervised node classification. It integrates global linear attention for efficient long-range dependency capture and a dual constraint module for local propagation. The dual constraint consists of (1) column-wise random masking on the representation matrix to dynamically limit redundant information aggregation, and (2) row-wise contrastive constraint to explicitly increase inter-node distance and preserve distinctiveness. This design achieves linear-complexity global mixing while effectively countering representation homogenization. Extensive evaluations on seven real-world datasets demonstrate that GLADC delivers competitive performance and maintains robustness in deep architectures (up to 32 layers). An ablation study further confirms the synergistic effect of both constraints in alleviating over-smoothing and preventing premature convergence.