  The majority of data scientists and machine learning practitioners use
relational data in their work [State of ML and Data Science 2017, Kaggle,
Inc.]. But training machine learning models on data stored in relational
databases requires significant data extraction and feature engineering efforts.
These efforts are not only costly, but they also destroy potentially important
relational structure in the data. We introduce a method that uses Graph Neural
Networks to overcome these challenges. Our proposed method outperforms
state-of-the-art automatic feature engineering methods on two out of three
datasets.
