# Hierarchical Graph Neural Networks for Particle Track Reconstruction

arXiv: https://arxiv.org/abs/2303.01640 · HTML: https://mlsanity.com/paper/2303.01640/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)
- [Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking](https://mlsanity.com/paper/2103.06995/) (2021)
- [Hierarchical graph neural nets can capture long-range interactions](https://mlsanity.com/paper/2107.07432/) (2021)

## Built on by (most cited first, 7 of 7)

- [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024)
- [Modeling the Real World with High-Density Visual Particle Dynamics](https://mlsanity.com/paper/2406.19800/) (2024)
- [ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset](https://mlsanity.com/paper/2512.15230/) (2025)
- [Graph Neural Network for Neutrino Physics Event Reconstruction](https://mlsanity.com/paper/2403.11872/) (2024)
- [Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2504.21844/) (2025)
- [Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems](https://mlsanity.com/paper/2507.19205/) (2025)
- [HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction](https://mlsanity.com/paper/2606.20437/) (2026)

## Abstract (arXiv)

We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which correspond to tracks and assigns spacepoints to these tracks, allowing disconnected spacepoints to be assigned to the same track, as well as multiple tracks to share the same spacepoint. We propose a novel learnable pooling algorithm called GMPool to generate these higher-level representations called "super-nodes", as well as a new loss function designed for tracking problems and HGNN specifically. On a standard tracking problem, we show that, compared with previous ML-based tracking algorithms, the HGNN has better tracking efficiency performance, better robustness against inefficient input graphs, and better convergence compared with traditional GNNs.