mlsanityData to 7 Oct 2026

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

arXiv 2203.12852 ↗First posted 23 Mar 2022hep-ex cs.LG hep-phcited by 12

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Abstract (arXiv)

Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs. This allows a wide variety of high- and low-level physical features to be attached to measurements and, by the same token, a wide variety of HEP tasks to be accomplished by the same GNN architectures. GNNs have found powerful use-cases in reconstruction, tagging, generation and end-to-end analysis. With the wide-spread adoption of GNNs in industry, the HEP community is well-placed to benefit from rapid improvements in GNN latency and memory usage. However, industry use-cases are not perfectly aligned with HEP and much work needs to be done to best match unique GNN capabilities to unique HEP obstacles. We present here a range of these capabilities, predictions of which are currently being well-adopted in HEP communities, and which are still immature. We hope to capture the landscape of graph techniques in machine learning as well as point out the most significant gaps that are inhibiting potentially large leaps in research.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 93 of its 165 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

93 of 165 references.

References · 93

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38 more
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50 most cited of 93 All 93 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 1; 2025: 0; 2026: 4.

12 papers.

  1. 2022Lorentz group equivariant autoencoderscited by 15
  2. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  3. 2024Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physicscited by 12
  4. 2023Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific MLcited by 8
  5. 2023Topological Reconstruction of Particle Physics Processes using Graph Neural Networkscited by 8
  6. 2022Explainable AI for High Energy Physicscited by 7
  7. 2022End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networkscited by 6
  8. 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physicscited by 6
  9. 2026Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Planecited by 3
  10. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 1
  11. 2026Dissecting Jet-Tagger Through Mechanistic Interpretabilitycited by 0
  12. 2026Probing SMEFT Operators through tbarttbart Production with Hyper-Graph Neural Networks at the LHCcited by 0