High-dimensional tabular data are common in biomedical and clinical research, yet conventional machine learning methods often struggle in such settings due to data scarcity, feature redundancy, and limited generalization. In this study, we systematically evaluate Synolitic Graph Neural Networks (SGNNs), a framework that transforms high-dimensional samples into sample-specific graphs by training ensembles of low-dimensional pairwise classifiers and analyzing the resulting graph structure with Graph Neural Networks. We benchmark convolution-based (GCN) and attention-based (GATv2) models across 15 UCI datasets under two training regimes: a foundation setting that concatenates all datasets and a dataset-specific setting with macro-averaged evaluation. We further assess cross-dataset transfer, robustness to limited training data, feature redundancy, and computational efficiency, and extend the analysis to a real-world ovarian cancer proteomics dataset. The results show that topology-aware node feature augmentation provides the dominant performance gains across all regimes. In the foundation setting, GATv2 achieves an ROC-AUC of up to 92.22 (GCN: 91.22), substantially outperforming XGBoost (86.05), $\alpha =0.001$. In the dataset-specific regime, GATv2, combined with minimum-connectivity filtering, achieves a macro ROC-AUC of 83.12, compared to 80.28 for XGBoost. Leave-one-dataset-out evaluation confirms cross-domain transfer, with an ROC-AUC of up to 81.99. SGNNs maintain ROC-AUC around 85% with as little as 10% of the training data and consistently outperform XGBoost in more extreme low-data regimes, $\alpha =0.001$. On ovarian cancer proteomics data, foundation training improves both predictive performance and stability. Efficiency analysis shows that graph filtering substantially reduces training time, inference latency, and memory usage without compromising accuracy. Overall, these findings suggest that SGNNs provide a robust and scalable approach for learning from high-dimensional, heterogeneous tabular data, particularly in biomedical settings with limited sample sizes.

In this study, we present a systematic evaluation of Synolitic Graph Neural Networks (SGNNs), a novel framework that transforms high-dimensional tabular data into sample-specific graphs using ensembles of low-dimensional pairwise classifiers. We demonstrate that augmenting these graphs with topology-aware node descriptors (such as degree, strength, closeness, and betweenness centrality) and applying graph sparsification techniques, either via minimum spanning connectivity or fixed-probability edge retention, can significantly improve classification performance. We evaluate both convolution-based (GCN) and attention-based graph neural networks (GATv2) across two training regimes: a foundation model setting where multiple datasets are concatenated, and dataset-specific training. Results show that attention-based models generally exhibit superior performance across classification tasksвАФin the foundation regime, dense (non-sparsified) graphs with node features yield 92.83 ROC-AUC for GATv2 and 92.34 for GCN (vs. 90.80 for XGBoost), and in the dataset-specific regime, GATv2 with minimal connectivity and node features reaches 88.96 ROC-AUC (vs. 86.84 for XGBoost). A leave-one-dataset-out evaluation further indicates out-of-domain transfer to previously unseen datasets (mean ROC-AUC: 0.78 with node features; 0.71 with maximum-threshold sparsification; 0.70 without features). Importantly, we demonstrate that the SGNN framework is capable of overcoming the curse of dimensionality, outperforming traditional machine learning models such as XGBoost in scenarios where the number of features exceeds the number of training samplesвАФmaintaining ROC-AUC above 80\% with only 5\% of the training data, while XGBoost drops to 60\%. Furthermore, SGNNs exhibit robustness to feature redundancy and correlation, with duplicating all features and adding noise producing only minor deviations, reducing the need for manual feature engineering or dimensionality reduction. Across all settings, SGNNs enhanced with node features consistently outperform XGBoost baselines, underscoring the effectiveness of integrating graph-based structural representations, topology-aware augmentation, and controlled sparsification in classification tasks.