  Tabular data prediction (TDP) is one of the most popular industrial
applications, and various methods have been designed to improve the prediction
performance. However, existing works mainly focus on feature interactions and
ignore sample relations, e.g., users with the same education level might have a
similar ability to repay the debt. In this work, by explicitly and
systematically modeling sample relations, we propose a novel framework TabGNN
based on recently popular graph neural networks (GNN). Specifically, we firstly
construct a multiplex graph to model the multifaceted sample relations, and
then design a multiplex graph neural network to learn enhanced representation
for each sample. To integrate TabGNN with the tabular solution in our company,
we concatenate the learned embeddings and the original ones, which are then fed
to prediction models inside the solution. Experiments on eleven TDP datasets
from various domains, including classification and regression ones, show that
TabGNN can consistently improve the performance compared to the tabular
solution AutoFE in 4Paradigm.
