  Traditional machine learning assumes samples in tabular data to be
independent and identically distributed (i.i.d). This assumption may miss
useful information within and between sample relationships in representation
learning. This paper relaxes the i.i.d assumption to learn tabular data
representations by incorporating between-sample relationships for the first
time using graph neural networks (GNN). We investigate our hypothesis using
several GNNs and state-of-the-art (SOTA) deep attention models to learn the
between-sample relationship on ten tabular data sets by comparing them to
traditional machine learning methods. GNN methods show the best performance on
tabular data with large feature-to-sample ratios. Our results reveal that
attention-based GNN methods outperform traditional machine learning on five
data sets and SOTA deep tabular learning methods on three data sets.
Between-sample learning via GNN and deep attention methods yield the best
classification accuracy on seven of the ten data sets. This suggests that the
i.i.d assumption may not always hold for most tabular data sets.
