Key papers

• Machine Can Automatically Discover Parametric Functions to Model HEP Data (2026)
  Ho Fung Tsoi et al.
  4.33
• Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane (2026)
  Pahal D. Patel et al.
  1.78
• GNN For Muon Particle Momentum estimation (2026)
  Vishak K Bhat et al.
  1.72
• Characterization and upgrade of a quantum graph neural network for charged particle tracking (2026)
  Matteo Argenton et al.
  1.72
• Vision Transformers and Graph Neural Networks for Charged Particle Tracking in the ATLAS Muon Spectrometer (2026)
  Jonathan Renusch (on behalf of the ATLAS Collaboration)
  1.72
• DCTracks: An Open Dataset for Machine Learning-Based Drift Chamber Track Reconstruction (2026)
  Qian Liyan et al.
  1.67
• Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml (2026)
  Katya Govorkova et al.
  1.67
• E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features (2025)
  Md Raqibul Islam et al.
  1.56
• Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction (2025)
  Shitij Govil et al.
  1.44
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction (2025)
  Partha Konar et al.
  1.39
• JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs (2025)
  Zhiqiang Que et al.
  1.33
• Guided Graph Compression for Quantum Graph Neural Networks (2025)
  Mikel Casals et al.
  1.22
• Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks (2025)
  William Sutcliffe et al.
  1.11
• Track Seeding and Labelling with Embedded-space Graph Neural Networks (2020)
  Nicholas Choma et al.
  —
• Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics (2020)
  Yutaro Iiyama et al.
  —
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics (2020)
  Raghav Kansal and Javier Duarte and Breno Orzari and Thiago Tomei and Maurizio Pierini and Mary Touranakou and Jean-Roch Vlimant and Dimitrios Gunopulos
  —
• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs (2020)
  Aneesh Heintz and Vesal Razavimaleki and Javier Duarte and Gage DeZoort and Isobel Ojalvo and Savannah Thais and Markus Atkinson and Mark Neubauer and Lindsey Gray and Sergo Jindariani and Nhan Tran and Philip Harris and Dylan Rankin and Thea Aarrestad and Vladimir Loncar and Maurizio Pierini and Sioni Summers and Jennifer Ngadiuba and Mia Liu and Edward Kreinar and Zhenbin Wu
  —
• Jet tagging in the Lund plane with graph networks (2020)
  Fr\'ed\'eric A. Dreyer and Huilin Qu
  —
• Hybrid Quantum-Classical Graph Convolutional Network (2021)
  Samuel Yen-Chi Chen et al.
  —
• Charged particle tracking via edge-classifying interaction networks (2021)
  Gage DeZoort et al.
  —
• Segmentation of EM showers for neutrino experiments with deep graph neural networks (2021)
  Vladislav Belavin et al.
  —
• Explaining machine-learned particle-flow reconstruction (2021)
  Farouk Mokhtar et al.
  —
• Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance (2021)
  Steven Tsan et al.
  —
• Graph Neural Networks for Charged Particle Tracking on FPGAs (2021)
  Abdelrahman Elabd and Vesal Razavimaleki and Shi-Yu Huang and Javier Duarte and Markus Atkinson and Gage DeZoort and Peter Elmer and Scott Hauck and Jin-Xuan Hu and Shih-Chieh Hsu and Bo-Cheng Lai and Mark Neubauer and Isobel Ojalvo and Savannah Thais and Matthew Trahms
  —
• Semi-Equivariant GNN Architectures for Jet Tagging (2022)
  Daniel Murnane et al.
  —
• Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges (2022)
  Savannah Thais et al.
  —
• End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks (2022)
  Shah Rukh Qasim et al.
  —
• IRC-safe Graph Autoencoder for unsupervised anomaly detection (2022)
  Oliver Atkinson et al.
  —
• Partition Pooling for Convolutional Graph Network Applications in Particle Physics (2022)
  M. Bachlechner (1) et al.
  —
• Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube (2022)
  R. Abbasi et al.
  —
• GraphNeT: Graph neural networks for neutrino telescope event reconstruction (2022)
  Andreas S{\o}gaard et al.
  —
• Jet tagging algorithm of graph network with HaarPooling message passing (2022)
  Fei Ma et al.
  —
• Do graph neural networks learn traditional jet substructure? (2022)
  Farouk Mokhtar et al.
  —
• Hierarchical Graph Neural Networks for Particle Track Reconstruction (2023)
  Ryan Liu et al.
  —
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks (2023)
  Lukas Ehrke et al.
  —
• Equivariant Graph Neural Networks for Charged Particle Tracking (2023)
  Daniel Murnane et al.
  —
• Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs (2023)
  Shi-Yu Huang et al.
  —
• Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations (2023)
  Gage DeZoort et al.
  —
• Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events (2023)
  Boyang Yu et al.
  —
• Graph Structure from Point Clouds: Geometric Attention is All You Need (2023)
  Daniel Murnane
  —
• PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions (2023)
  Yash Semlani et al.
  —
• HyperTrack: Neural Combinatorics for High Energy Physics (2023)
  Mikael Mieskolainen
  —
• Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks (2023)
  Savannah Thais et al.
  —
• High Pileup Particle Tracking with Object Condensation (2023)
  Kilian Lieret et al.
  —
• Combined track finding with GNN & CKF (2024)
  Lukas Heinrich et al.
  —
• Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics (2024)
  Siqi Miao et al.
  —
• A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics (2024)
  Emanuel Pfeffer and Michael Wa{\ss}mer and Yee-Ying Cung and Roger Wolf and Ulrich Husemann
  —
• Integrating Physics Inspired Features with Graph Convolution (2024)
  Rameswar Sahu
  —
• Graph Neural Network for Neutrino Physics Event Reconstruction (2024)
  V Hewes and Adam Aurisano and Giuseppe Cerati and Jim Kowalkowski and Claire Lee and Wei-keng Liao and Daniel Grzenda and Kaushal Gumpula and Xiaohe Zhang
  —
• Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques (2024)
  A. Verdone et al.
  —
• Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics (2024)
  Md Abrar Jahin et al.
  —
• Lie-Equivariant Quantum Graph Neural Networks (2024)
  Jogi Suda Neto et al.
  —