  Particle physics is a branch of science aiming at discovering the fundamental
laws of matter and forces. Graph neural networks are trainable functions which
operate on graphs---sets of elements and their pairwise relations---and are a
central method within the broader field of geometric deep learning. They are
very expressive and have demonstrated superior performance to other classical
deep learning approaches in a variety of domains. The data in particle physics
are often represented by sets and graphs and as such, graph neural networks
offer key advantages. Here we review various applications of graph neural
networks in particle physics, including different graph constructions, model
architectures and learning objectives, as well as key open problems in particle
physics for which graph neural networks are promising.
