# GraphNeT: Graph neural networks for neutrino telescope event reconstruction

arXiv: https://arxiv.org/abs/2210.12194 · HTML: https://mlsanity.com/paper/2210.12194/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory](https://mlsanity.com/paper/2101.11589/) (2021)
- [Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube](https://mlsanity.com/paper/2209.03042/) (2022)

## Built on by (most cited first, 3 of 3)

- [Learning Efficient Representations of Neutrino Telescope Events](https://mlsanity.com/paper/2410.13148/) (2024)
- [NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes](https://mlsanity.com/paper/2511.13111/) (2025)
- [HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction](https://mlsanity.com/paper/2606.20437/) (2026)

## Abstract (arXiv)

GraphNeT is an open-source python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using graph neural networks (GNNs). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques. GNNs from GraphNeT are flexible enough to be applied to data from all neutrino telescopes, including future projects such as IceCube extensions or P-ONE. This means that GNN-based reconstruction can be used to provide state-of-the-art performance on most reconstruction tasks in neutrino telescopes, at real-time event rates, across experiments and physics analyses, with vast potential impact for neutrino and astro-particle physics.