Advancing Tabular Data Classification with Graph Neural Networks: A Random Forest Proximity Method

Graphs are essential for modeling complex relationships, analyzing networks, and offering versatile representations that capture diverse data structures. Graph Neural Networks (GNNs) excel in processing graph-structured data by leveraging the relational information encoded in graph topology. However, not all types of data possess an explicit graph structure, particularly tabular data, which is ubiquitous in the real world. To enable the use of GNNs for tabular data, it is necessary to convert tabular data into graph-structured data. Existing methods for this conversion often lack a generic, straightforward approach with unrestrictive assumptions that can directly apply GNNs to tabular data for downstream tasks. In this paper, we introduce RF-GNN, a novel method that enhances traditional machine learning approaches by transforming tabular data into graph structures and leveraging GNNs. Our approach calculates the similarity between pairs of samples based on Random Forest (RF) proximities, which measure how often the same pair appears in the same terminal nodes of a tree in a Random Forest. This enables the creation of an adjacency matrix for instances of tabular data, allowing the application of GNNs. Extensive experiments on 36 different datasets demonstrate that RF-GNN consistently outperforms traditional machine learning models and recent methods in terms of weighted F1-score. We conduct additional experiments to evaluate the effectiveness of RF-GNN components and settings. The code is available in https://github.com/DSAatUSU/RF-GNN.