Relational inductive biases, deep learning, and graph networks

The purpose of this paper is to explore relational inductive biases in modern AI, especially deep learning, describing a rough taxonomy of existing approaches, and introducing a common mathematical framework for expressing and unifying various approaches. The key theme running through this work is structure—how the world is structured, and how the structure of different computational strategies determines their strengths and weaknesses.