Overcoming the Curse of Dimensionality with Synolitic AI

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.