  Synthetic data generation has recently gained widespread attention as a more
reliable alternative to traditional data anonymization. The involved methods
are originally developed for image synthesis. Hence, their application to the
typically tabular and relational datasets from healthcare, finance and other
industries is non-trivial. While substantial research has been devoted to the
generation of realistic tabular datasets, the study of synthetic relational
databases is still in its infancy. In this paper, we combine the variational
autoencoder framework with graph neural networks to generate realistic
synthetic relational databases. We then apply the obtained method to two
publicly available databases in computational experiments. The results indicate
that real databases' structures are accurately preserved in the resulting
synthetic datasets, even for large datasets with advanced data types.
