<![CDATA[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.]]>
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