Graph Attention Diffusion Method Combining Diffusion Mechanism and Graph Attention Mechanism

Graph neural networks have attracted much attention and performed well in many downstream tasks. However, due to issues such as oversmoothing, existing graph neural networks are limited in their ability to quantitatively exploit higher-order neighborhood information. This paper introduces GAtD (Graph Attention Diffusion Method), which propagates attention to a wider range and aggregates higher-order information. We theoretically analyze the effectiveness of GAtD and demonstrate the convergence and linear complexity. A series of experiments demonstrates that, by combining diffusion and attention mechanisms, our method can effectively capture deep level relationships between nodes.