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Tabular data prediction (TDP) is one of the most popular industrialapplications, and various methods have been designed to improve the predictionperformance. However, existing works mainly focus on feature interactions andignore sample relations, e.g., users with the same education level might have asimilar ability to repay the debt. In this work, by explicitly andsystematically modeling sample relations, we propose a novel framework TabGNNbased on recently popular graph neural networks (GNN). Specifically, we firstlyconstruct a multiplex graph to model the multifaceted sample relations, andthen design a multiplex graph neural network to learn enhanced representationfor each sample. To integrate TabGNN with the tabular solution in our company,we concatenate the learned embeddings and the original ones, which are then fedto prediction models inside the solution. Experiments on eleven TDP datasetsfrom various domains, including classification and regression ones, show thatTabGNN can consistently improve the performance compared to the tabularsolution AutoFE in 4Paradigm.

TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction