Graphs are essential for modeling complex relationships and capturing structured interactions in data. Graph Neural Networks (GNNs) are particularly effective when such relational structure is explicitly available, but many real-world datasets, most notably tabular data, lack an inherent graph representation. To address this limitation, we propose RF-GNN, a framework that constructs instance-level graphs from tabular data using proximity measures induced by random forests. These proximities capture nonlinear feature interactions and data-adaptive similarity without imposing restrictive assumptions on feature geometry. The resulting graphs enable the direct application of GNNs to tabular learning problems. Extensive experiments on 36 benchmark datasets demonstrate that RF-GNN consistently outperforms strong classical baselines and recent graph-construction methods in terms of weighted F1-score. Additional ablation studies highlight the impact of proximity design choices and graph construction settings.

翻译：图是建模复杂关系与捕捉数据中结构化交互的关键工具。当此类关系结构显式可用时，图神经网络（GNNs）尤为有效，但许多现实世界数据集（尤其是表格数据）缺乏固有的图表示。为应对这一局限，我们提出了RF-GNN框架，该框架利用随机森林诱导的邻近性度量，从表格数据构建实例级图。这些邻近性度量能够捕捉非线性特征交互与数据自适应的相似性，而无需对特征几何结构施加严格假设。生成的图使得GNN能够直接应用于表格学习问题。在36个基准数据集上的大量实验表明，RF-GNN在加权F1分数方面持续优于强经典基线方法与近期图构建方法。进一步的消融研究揭示了邻近性设计选择与图构建设置的影响。