LDA-GCN: Localized Differential Attention for Graph Convolutional Network

Graph Convolutional Networks (GCNs) have demonstrated significant efficacy in graph representation learning. Existing approaches often rely on stacking multiple GCN layers to incorporate high-order neighborhood information. How-ever, deeper GCN architectures tend to learn similar node representations by more focusing on the global topology of the graph, thereby diminishing the influence of local structures and leading to over-smoothing. To address this, we propose a Localized Differential Attention Graph Convolutional Network (LDA -GCN), designed to enhance the preservation of local structural information. LDA-GCN introduces a localized differential attention mechanism that diversifies node representations and includes a drop aggregation module to strengthen the robustness of this differential attention. Extensive experiments conducted on several benchmark datasets confirm that LDA-GCN achieves superior performance compared to state-of-the-art methods.