Graph neural networks (GNN) have shown strong performance in node classification tasks, yet most existing models rely on uniform or shared weight aggregation, lacking flexibility in modeling the varying strength of relationships among nodes. This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node. Unlike traditional methods, the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance. The model operates in the spatial domain, utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation. Extensive experiments are conducted on five graph-structured datasets, including Cora, Citeseer, PubMed, Reddit, and BlogCatalog, as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset (OULAD) educational platform. Results demonstrate that the proposed model achieves good classification accuracy, while significantly reducing training time through direct second-order neighbor fusion and data preprocessing. Moreover, analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead, confirming the efficiency of first- and second-order convolution in practical applications. Overall, the proposed graph coupling model offers a lightweight, interpretable, and effective framework for multi-label node classification in complex networks.

Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways from both structural and feature perspectives. Drawing on representative studies, we identify three major integration patterns: cascaded fusion, where heterogeneous modules such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and GraphSAGE are sequentially combined for hierarchical feature learning; parallel fusion, where multi-branch architectures jointly encode complementary graph features; and feature-level fusion, which employs concatenation, weighted summation, or attention-based gating to adaptively merge multi-source embeddings. Through these patterns, integrated GNNs achieve enhanced expressiveness, robustness, and scalability across domains including transportation, biomedicine, and cybersecurity.