Research on Graph Feature Data Aggregation Algorithm Based on Graph Convolution and Attention Mechanism

I Aiming at the wide application of graph data in the field of machine learning and deep learning, this paper proposes a novel graph feature data aggregation algorithm. The algorithm is an innovative fusion of Graph Convolutional Networks (GCN) and Attention mechanisms. Traditional graph convolution methods may have problems of information loss and excessive smoothing when processing complex graph structure data, but the attention mechanism can dynamically assign different weights to different nodes or edges, so as to effectively capture key features in the graph and improve the learning ability of the model. In this paper, we first review the basic principles of graph neural networks and attention mechanisms, and then elaborate the proposed algorithm framework. By embedding an adaptive attention mechanism in the graph convolution layer, the algorithm not only enhances the selective extraction ability of node neighborhood features, but also enables the model to adaptively adjust the information aggregation process according to the importance of the relationship between nodes, thus improving the differentiation of feature representation and the generalization performance of the model. In order to verify the effectiveness of the proposed algorithm, we carried out validation on different experimental tasks.