Graph-Based Machine Learning for Modeling Complex Relationships in High-Dimensional Data
Article Sidebar
Graph Neural Networks,
High-Dimensional Data,
Relational Learning,
Scalable Machine Learning
Main Article Content
Sri Huning Anwariningsih
Lusiana Efrizoni
Misinem Misinem
Abstract
Background: High-dimensional datasets frequently encode complex relational dependencies that are inadequately captured by conventional vector-based machine learning models operating under independent and identically distributed assumptions.
Aims: This study aims to develop an end-to-end graph-based machine learning pipeline that systematically converts high-dimensional structured data into relational graph representations.
Methods: The proposed methodology constructs relational graphs from raw feature spaces using similarity-based and interaction-driven edge formation strategies. The framework integrates scalable graph neural network training with neighborhood sampling techniques and structural attribution analysis. Comparative benchmarking is conducted against strong non-relational baselines, including multilayer perceptrons, Random Forests, and gradient boosting models. Evaluation employs standardized train validation–test splits, cross-validation protocols, and statistical significance testing across multiple heterogeneous datasets.
Result: Experimental findings show that graph-based architectures consistently outperform non-relational baselines, achieving improvements of 6–12% in F1-score and ROC-AUC across diverse datasets. Graph Attention Networks demonstrate enhanced robustness under noisy conditions and provide interpretable neighbor importance distributions. Scalability experiments confirm that sampling-based training preserves computational feasibility for large-scale graphs without substantial performance degradation.
Conclusion: The results demonstrate that incorporating relational inductive bias through graph modeling significantly enhances predictive accuracy, robustness, and structural interpretability in high-dimensional environments.
Article Details
Copyright (c) 2026 Sri Huning Anwariningsih, Lusiana Efrizoni, Misinem Misinem
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Abdullah, D. A., Hamad, D. R., Maolood, I. Y., Beitollahi, H., Ameen, A. K., Aula, S. A., Abdulla, A. A., Shakor, M. Y., & Muhamad, S. S. (2025). A novel facial recognition technique with focusing on masked faces. Ain Shams Engineering Journal, 16(5), 103350. https://doi.org/10.1016/j.asej.2025.103350
Alghamdi, T. A., & Javaid, N. (2022). A Survey of Preprocessing Methods Used for Analysis of Big Data Originated from Smart Grids. IEEE Access, 10, 29149–29171. https://doi.org/10.1109/ACCESS.2022.3157941
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., & Murphy, K. (2022). Machine Learning on Graphs: A Model and Comprehensive Taxonomy. Journal of Machine Learning Research, 23(89), 1–64. http://jmlr.org/papers/v23/20-852.html
Chen, L., Ding, J., Song, D., & Qu, Z. (2025). Exploring scientific contributions through citation context and division of labor. Scientometrics 2025 130:5, 130(5), 2901–2921. https://doi.org/10.1007/s11192-025-05318-x
Chowdhury, S. H., Sany, M. R., Ahamed, M. H., Das, S. K., Badal, F. R., Das, P., Tasneem, Z., Hasan, M. M., Islam, M. R., Ali, M. F., Abhi, S. H., Islam, M. M., & Sarker, S. K. (2023). A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models. International Journal of Intelligent Systems, 2023(1), 8674641. https://doi.org/10.1155/2023/8674641
Christodoulou, M. (2022). Georg Simmel and the ontology of relational emergence. Journal for the Theory of Social Behaviour, 52(1), 49–66. https://doi.org/10.1111/jtsb.12294
Freiesleben, T., & Grote, T. (2023). Beyond generalization: a theory of robustness in machine learning. Synthese 2023 202:4, 202(4), 109-. https://doi.org/10.1007/s11229-023-04334-9
Fu, R., Cai, M., Wang, S., Wang, Y., & Jia, C. (2024). Exploring non-Euclidean relationships in EEG emotion recognition: A dynamic graph attention network. Biomedical Signal Processing and Control, 94, 106276. https://doi.org/10.1016/j.bspc.2024.106276
Gan, X., Li, T., Gong, C., Li, D., Dong, D., Liu, J., Lu, K., & Graphcsr, K. L. (2025). Degree-Equalized CSR Format for Large-scale Graph Processing. 18(11), 4255–4268. https://doi.org/10.14778/3749646.3749691
Gholizadeh, N., & Musilek, P. (2024). Explainable reinforcement learning for distribution network reconfiguration. Energy Reports, 11, 5703–5715. https://doi.org/10.1016/j.egyr.2024.05.031
Gong, J., & Chen, T. (2024). Deep Configuration Performance Learning: A Systematic Survey and Taxonomy. ACM Transactions on Software Engineering and Methodology, 34(1). https://doi.org/10.1145/3702986
Han, E., Kwon, H., & Jung, I. (2025). A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets. BMC Genomics 2025 26:1, 26(1), 769-. https://doi.org/10.1186/s12864-025-11925-y
Jiang, L., Jin, Q., Hua, F., Jiang, X., Wang, Z., Gao, W., Huang, F., Fang, C., & Yang, Y. (2024). Numerical investigation of the effective receptive field and its relationship with convolutional kernels and layers in convolutional neural network. Frontiers in Marine Science, 11, 1492572. https://doi.org/10.3389/fmars.2024.1492572
Khemani, B., Patil, S., Kotecha, K., & Tanwar, S. (2024). A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. Journal of Big Data 2024 11:1, 11(1), 18-. https://doi.org/10.1186/s40537-023-00876-4
Kucik, A., & Stokholm, A. (2023). AI4SeaIce: selecting loss functions for automated SAR sea ice concentration charting. Scientific Reports 2023 13:1, 13(1), 5962-. https://doi.org/10.1038/s41598-023-32467-x
Li, H., Zhang, L., Li, J., Wu, Q., Qian, L., He, J., Ni, Y., Kovatcheva-Datchary, P., Yuan, R., Liu, S., Shen, L., Zhang, M., Sheng, B., Li, P., Kang, K., Wu, L., Fang, Q., Long, X., Wang, X., … Jia, W. (2024). Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota. Nature Metabolism, 6(3), 578–597. https://doi.org/10.1038/s42255-024-00988-y
Li, C. Te, Tsai, Y. C., Chen, C. Y., & Liao, J. C. (2025). Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions. ACM Computing Surveys, 58(1), 16. https://doi.org/10.1145/3744918
Lin, H., Yan, M., Ye, X., Fan, D., Pan, S., Chen, W., & Xie, Y. (2023). A Comprehensive Survey on Distributed Training of Graph Neural Networks. Proceedings of the IEEE, 111(12), 1572–1606. https://doi.org/10.1109/JPROC.2023.3337442
Liu, Z., Zhang, S., Shao, X., & Wu, Z. (2023). Accurate and efficient urban wind prediction at city-scale with memory-scalable graph neural network. Sustainable Cities and Society, 99, 104935. https://doi.org/10.1016/j.scs.2023.104935
Marconi, L., & Cabitza, F. (2025). Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI. International Journal of Medical Informatics, 202(1), 105970. https://doi.org/10.1016/j.ijmedinf.2025.105970
Munikoti, S., Das, L., & Natarajan, B. (2022). Scalable graph neural network-based framework for identifying critical nodes and links in complex networks. Neurocomputing, 468, 211–221. https://doi.org/10.1016/j.neucom.2021.10.031
Myren, S., Parikh, N., Rael, R., Flynn, G., Higdon, D., & Casleton, E. (2026). Evaluation of Seismic Artificial Intelligence with Uncertainty. Seismological Research Letters, 97(1), 471–486. https://doi.org/10.1785/0220240444
Narayan, D. (2022). Platform capitalism and cloud infrastructure: Theorizing a hyper-scalable computing regime. Environment and Planning A, 54(5), 911–929. https://doi.org/10.1177/0308518X221094028
Noschese, S., & Reichel, L. (2024). Edge importance in complex networks. Numerical Algorithms 2024 99:1, 99(1), 377–410. https://doi.org/10.1007/s11075-024-01881-1
Oneto, L., Navarin, N., Biggio, B., Errica, F., Micheli, A., Scarselli, F., Bianchini, M., Demetrio, L., Bongini, P., Tacchella, A., & Sperduti, A. (2022). Towards learning trustworthily, automatically, and with guarantees on graphs: An overview. Neurocomputing, 493, 217–243. https://doi.org/10.1016/j.neucom.2022.04.072
Park, J., Yun, S., Park, H., Kang, J., Jeong, J., Kim, K. M., Ha, J. W., & Kim, H. J. (2025). Deformable Graph Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(7), 5385–5396. https://doi.org/10.1109/TPAMI.2025.3550281
Peng, M., Liu, Y., Qadri, I. A., Bhatti, U. A., Ahmed, B., Sarhan, N. M., & Awwad, E. M. (2024). Advanced image segmentation for precision agriculture using CNN-GAT fusion and fuzzy C-means clustering. Computers and Electronics in Agriculture, 226, 109431. https://doi.org/10.1016/j.compag.2024.109431
Samantaray, A. K., Mooney, P. A., & Vivacqua, C. A. (2024). Bergen metrics: Composite error metrics for assessing performance of climate models using EURO-CORDEX simulations. Geoscientific Model Development, 17(8), 3321–3339. https://doi.org/10.5194/gmd-17-3321-2024
Santos, C. F. G. Dos, & Papa, J. P. (2022). Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks. ACM Computing Surveys, 54(10 s). https://doi.org/10.1145/3510413
Silva, G., & Rodriguez, P. (2024). Fine-tuning adaptive stochastic optimizers: determining the optimal hyperparameter $$epsilon$$ via gradient magnitude histogram analysis. Neural Computing and Applications 2024 36:35, 36(35), 22223–22243. https://doi.org/10.1007/s00521-024-10302-2
Singh, S. K., Rai, R., Khawale, R. P., Patel, D., Bielecki, D., Nguyen, R., Wang, J., & Zhang, Z. (2024). Deep Learning in Computational Design Synthesis: A Comprehensive Review. Journal of Computing and Information Science in Engineering, 24(4). https://doi.org/10.1115/1.4064215
Sivakumar, M., Parthasarathy, S., & Padmapriya, T. (2024). A simplified approach for efficiency analysis of machine learning algorithms. PeerJ Computer Science, 10, 1–25. https://doi.org/10.7717/peerj-cs.2418
Sun, X., & Jun, Z. (2025). Interpretable Large-Scale Graph Network Model: A Computational Platform for Predicting Instructing Achievement Bias. IEEE Access, 13, 156301–156317. https://doi.org/10.1109/ACCESS.2025.3605056
Sun, Y., & Gao, J. (2025). HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer. BMC Bioinformatics 2025 26:1, 26(1), 49-. https://doi.org/10.1186/s12859-025-06071-x
Tran, V. L., & Lee, J. (2025). A novel scalable graph neural network for response prediction of structures with different topologies. Engineering Structures, 343, 121146. https://doi.org/10.1016/j.engstruct.2025.121146
Vatter, J., Mayer, R., & Jacobsen, H. A. (2024). The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: ACM Computing Surveys, 56(1). https://doi.org/10.1145/3597428
Wang, Y. C., Xu, S., Ma, E., & Xu, F. (2025). Utilize OCB-C-Driven Learning Mechanism to Empower Employees as Good Citizens: Perspective of Trial-and-Error Learning Theory. Cornell Hospitality Quarterly, 66(2), 196–210. https://doi.org/10.1177/19389655241256535
Wu, Q., Bauer, D., Doyle, M. J., & Ma, K. L. (2024). Interactive Volume Visualization via Multi-Resolution Hash Encoding Based Neural Representation. IEEE Transactions on Visualization and Computer Graphics, 30(8), 5404–5418. https://doi.org/10.1109/TVCG.2023.3293121
Xiao, Y., Liu, D., Cui, L., & Wang, H. (2024). Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings. Mechanical Systems and Signal Processing, 220, 111679. https://doi.org/10.1016/j.ymssp.2024.111679
Yun, S., Nam, H., Park, J., Kim, B., Ahn, J. H., & Lee, E. (2024). GraNDe: Efficient Near-Data Processing Architecture for Graph Neural Networks. IEEE Transactions on Computers, 73(10), 2391–2404. https://doi.org/10.1109/TC.2023.3283677
Zhou, L., Du, G., Lü, K., Wang, L., & Du, J. (2024). A Survey and an Empirical Evaluation of Multi-View Clustering Approaches. ACM Computing Surveys, 56(7). https://doi.org/10.1145/3645108
Zou, X., Chen, C., Zhang, L., Li, S., Zhou, J. T., Wei, W., & Li, K. (2025). Efficient Message Passing Algorithm and Architecture Co-Design for Graph Neural Networks. IEEE Transactions on Emerging Topics in Computational Intelligence, 9(1), 889–903. https://doi.org/10.1109/TETCI.2024.3420692