# EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction

arXiv: https://arxiv.org/abs/2407.13925 · HTML: https://mlsanity.com/paper/2407.13925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [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)
- [The Tracking Machine Learning challenge : Throughput phase](https://mlsanity.com/paper/2105.01160/) (2021)
- [High Pileup Particle Tracking with Object Condensation](https://mlsanity.com/paper/2312.03823/) (2023)

## Built on by (most cited first, 1 of 1)

- [FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics](https://mlsanity.com/paper/2508.14087/) (2025)

## Abstract (arXiv)

Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising approach that can improve scalability. Most of these GNN-based methods, including the edge classification (EC) and the object condensation (OC) approach, require an input graph that needs to be constructed beforehand. In this work, we consider a one-shot OC approach that reconstructs particle tracks directly from a set of hits (point cloud) by recursively applying graph attention networks with an evolving graph structure. This approach iteratively updates the graphs and can better facilitate the message passing across each graph. Preliminary studies on the TrackML dataset show better track performance compared to the methods that require a fixed input graph.