Research on Graph Convolutional Neural Networks with Integrated Attention Mechanisms in Node Classification Tasks

Graph Convolutional Networks (GCNs) have demonstrated powerful performance in the task of node classification of graph-structured data. However, traditional GCN methods typically only consider information aggregation of immediate neighbors and assign the same weight to all neighbors, which limits the model's ability to capture complex relationships and long-distance dependencies. To solve these problems, this paper proposes an improved Attention-based Graph Convolutional Network (AGCN) that integrates Attention mechanisms. It aims to enhance the learning of node representation by introducing multipath adjacency matrix aggregation and attention-based feature fusion. Specifically, the AGCN first defines several different types of path patterns (by connection mode and similarity based path) to capture complex connections between nodes. For each path pattern, we construct the corresponding adjacency matrix and aggregate the information through the independent GCN layer to obtain the node feature representation under the path. Then, in order to effectively integrate features from different paths, we design an attentional mechanism module. This module can dynamically evaluate the importance of different path features, automatically adjust the weights according to their contributions to the target node classification, and realize the soft selection and fusion between features. Experimental results show that the proposed AGCN model significantly outperforms existing GCN variants in node classification tasks on multiple benchmark datasets. In particular, our approach performs well on graph data with complex structures and heterogeneous relationships, proving that the representation and generalization performance of graph convolutional networks can be effectively improved by combining multipath information and attention mechanisms.