Graph Attention

Graph Attention is a graph neural network method based on the attention mechanism, aimed at capturing complex relationships in graph structures by learning weight assignments between nodes. Its core objective is to enhance the representation learning capabilities of graph data, thereby achieving more accurate performance in tasks such as node classification, link prediction, and graph classification. Graph Attention effectively handles large-scale graph data by dynamically adjusting attention coefficients, maintaining computational efficiency while improving the model's interpretability and robustness. This approach has shown significant application value in fields such as social network analysis, bioinformatics, and recommendation systems.