Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes

Junmin Lyu¹, Guangyu Xu², Feng Bao^3,*, Yu Zhou⁴, Yuxin Liu⁵, Siyu Lu^5,*, Wenfeng Zheng⁵
1 School of Artificial Intelligence, Guangzhou Huashang University, Guangzhou, 511300, China
2 School of the Environment, The University of Queensland, St Lucia 2, Brisbane, QLD-4072, Australia
3 School of Biological and Environmental Engineering, Xi’an University, Xi’an, 710065, China
4 Research Institute of Al Convergence, Hanyang University ERICA, Ansan-si, 15588, Republic of Korea
5 School of Automation, University of Electronic Science and Technology of China, Chengdu, 611731, China
* Corresponding Author: Feng Bao. Email: email ; Siyu Lu. Email: email
(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2025.075239

Received 28 October 2025; Accepted 15 December 2025; Published online 29 December 2025

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Abstract

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.