TY  - EJOU
AU  - Lyu, Junmin 
AU  - Xu, Guangyu 
AU  - Bao, Feng 
AU  - Zhou, Yu 
AU  - Liu, Yuxin 
AU  - Lu, Siyu 
AU  - Zheng, Wenfeng 

TI  - Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - GNN; social networks nodes; multi-label classification model; graphic convolution neural network; coupling principle

DO  - 10.32604/cmes.2025.075239