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.

粒子物理学是科学的一个分支，旨在发现物质和力的基本定律。图神经网络是可训练的函数，可对图（元素集及其成对关系）进行操作，并且是更广泛的几何深度学习领域的中心方法。它们具有很高的表现力，并且在各个领域都表现出优于其他经典深度学习方法的性能。粒子物理学中的数据通常由集合和图形表示，因此，图形神经网络具有关键优势。在这里，我们回顾了图神经网络在粒子物理学中的各种应用，包括不同的图构造，模型体系结构和学习目标，以及图神经网络有望实现的粒子物理学中的关键开放问题。