  While there has been extensive work on deep neural networks for images and
text, deep learning for relational databases (RDBs) is still a rather
unexplored field.
  One direction that recently gained traction is to apply Graph Neural Networks
(GNNs) to RBDs. However, training GNNs on large relational databases (i.e.,
data stored in multiple database tables) is rather inefficient due to multiple
rounds of training and potentially large and inefficient representations.
Hence, in this paper we propose SPARE (Single-Pass Relational models), a new
class of neural models that can be trained efficiently on RDBs while providing
similar accuracies as GNNs. For enabling efficient training, different from
GNNs, SPARE makes use of the fact that data in RDBs has a regular structure,
which allows one to train these models in a single pass while exploiting
symmetries at the same time. Our extensive empirical evaluation demonstrates
that SPARE can significantly speedup both training and inference while offering
competitive predictive performance over numerous baselines.
