Abstract

Complex correlation datasets widely exist in social networks, citation networks, and biological systems, where traditional machine learning methods fail to effectively capture implicit topological correlation and high-dimensional feature interactions. Graph Neural Networks (GNNs) have unique advantages in processing non-Euclidean graph-structured data, which can realize adaptive feature mining and end-to-end node prediction. This paper proposes a multi-layer graph convolution feature mining model with adaptive neighbor aggregation, aiming at the problems of incomplete feature extraction and weak topological dependency perception in traditional GNNs for complex correlation datasets. Firstly, the graph structure normalization and feature dimensionality reduction are performed on the input complex graph data. Secondly, an improved graph convolution aggregation formula is constructed to fuse node attribute features and topological structure features hierarchically, and a residual connection mechanism is introduced to solve the gradient disappearance problem of deep graph networks. Finally, node classification prediction tasks are realized based on mined deep graph features. Comparative experiments are conducted on six classic complex graph benchmark datasets including Cora, Citeseer, Pubmed, Cornell, Texas, and Wisconsin. The experimental results show that the proposed model achieves average prediction accuracy improvements of 3.2%–8.7% compared with baseline models such as GCN, GAT, and GraphSAGE, which verifies the effectiveness and superiority of the proposed method in complex correlation data feature mining and node prediction.

Keywords

Complex Correlation DatasetFeature MiningNode PredictionGraph ConvolutionTopological Feature

Publication

Accepted 2026 July 20 ; Published 2026 July 26

Journal of Intelligence and Engineering Technology, 2026, 1(3), 3136-0939

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Metadata

DOI

10.70393/6a696574.343331

ARK

ark:/40704/JIET.v1n3a01

Pages

1–5

References

12

Disciplines

Intelligent Systems

Subjects

Other

Cite This Article

Acknowledgments

Not Applicable.

Funding

Not Applicable.

Institutional Review Board Statement

Not Applicable.

Data Availability Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Conflict of Interest

Not Applicable.

Author Contributions

Not application.

References

1. Yin, D., Jiang, R., Deng, J., Li, Y., Xie, Y., Wang, Z., ... & Shang, J. S. (2023). MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction. GeoInformatica, 27(1), 77-105.
2. Liu, T., & Zhang, J. (2023). An adaptive traffic flow prediction model based on spatiotemporal graph neural network: T. Liu, J. Zhang. The Journal of Supercomputing, 79(14), 15245-15269.
3. Wang, Y., Wang, Z., Wang, T., Hu, J., You, Z., & Peng, J. (2026). HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration. BMC biology, 24(1), 80.
4. Li, C., Liu, X., Yan, Y., Zhao, Z., & Zeng, Q. (2023). HetGNN-SF: Self-supervised learning on heterogeneous graph neural network via semantic strength and feature similarity: C. Li et al. Applied Intelligence, 53(19), 21902-21919.
5. Yu, Z., Li, B., Zhang, F., You, J., Li, B., Chen, Z., ... & Xiao, K. (2026). GTF: A New Interpretable Graph Neural Network for Geochemical Anomaly Detection in Mineral Prospectivity Mapping. Natural Resources Research, 35(2), 797-822.
6. Wang, Y., Rui, L., & Ma, J. (2023). A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks. Applied Soft Computing, 146, 110629.
7. Chen, Z., Cen, G., Wei, Y., & Li, Z. (2023). Student performance prediction approach based on educational data mining. IEEE Access, 11, 131260-131272.
8. Shi, J., Zhang, H., Zhou, J., & Tang, L. (2026, May). The Impact of Working Capital Management Efficiency on Corporate Financial Performance: A Panel Data Regression Analysis. In 2026 5th International Conference on Social Sciences and Humanities and Arts (SSHA 2026) (pp. 1350-1355). Atlantis Press.
9. Li, C., Guo, L., Zhang, H., Tang, L., Shi, J., & Zhou, J. (2026). Related-Party Transaction Fraud Detection with Data-Governance-Enhanced Graph Neural Networks. Available at SSRN 6610498.
10. Li, X., Fang, L., & Gao, Z. (2026, May). Features and Mechanisms of Computer-Mediated Language Information Interaction in Cross-Cultural Contexts. In 2026 5th International Conference on Social Sciences and Humanities and Arts (SSHA 2026) (pp. 979-985). Atlantis Press.
11. Cao, N., Zhang, W., Leng, N., Guo, Y., Yu, Z., He, Q., & Tang, H. (2026, February). Research on the Input-Output Efficiency Model of Robotic Process Automation (RPA) in Financial Supply Chains. In Proceedings of the 2nd International Conference on Digital Management and Information Technology (pp. 180-185).
12. Ni, H., Zhang, T., Liu, H., He, Z., Fan, Y., Zhang, W., & Zhang, H. (2026, January). Research and Design of Blockchain-based Propagation Algorithm for IP Tags Using Local Random Walk in Big Data Marketing. In 2026 International Conference on Communication Networks and Machine Learning (CNML) (pp. 284-288). IEEE.

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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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