Graph neural networks in particle physics

By Jonathan Shlomi, Peter W. Battaglia, Jean-Roch Vlimant. Machine Learning: Science and Technology, 2020

352 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/3f9d84e408dea70c41bc7ccb6ed1093f6402c10d

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Citations in the corpus

Utilizing graph machine learning within drug discovery and development (2021, methodology): “These methods have already been successfully applied to a variety of tasks and domains such as recommendation in social media and E-commerce [35–38], traffic estimations in Google Maps [39], misinformation detection in social media [40], and various domains of natural sciences including modelling fl”
A Survey on Oversmoothing in Graph Neural Networks (2023, neutral): “…et al. (2018), transportation Derrow-Pinion et al. (2021), computational chemistry (Gilmer et al., 2017), drug discovery Gaudelet et al. (2021), particle physics (Shlomi et al., 2020), and analysis of social networks (see Zhou et al. (2019); Bronstein et al. (2021) for additional applications).”
Understanding over-squashing and bottlenecks on graphs via curvature (2021, background): “, 2017) have become very popular in the machine learning community due to their ability to deal with broad classes of systems of relations and interactions, ranging from social networks to particle physics (Shlomi et al., 2021).”