# Jet tagging algorithm of graph network with HaarPooling message passing

arXiv: https://arxiv.org/abs/2210.13869 · HTML: https://mlsanity.com/paper/2210.13869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 25 Oct 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)
- [Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling](https://mlsanity.com/paper/1905.11577/) (2019)
- [Recursive Neural Networks in Quark/Gluon Tagging](https://mlsanity.com/paper/1711.02633/) (2017)
- [IRC-safe Graph Autoencoder for unsupervised anomaly detection](https://mlsanity.com/paper/2204.12231/) (2022)
- [Leveraging universality of jet taggers through transfer learning](https://mlsanity.com/paper/2203.06210/) (2022)

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

- [Jet Discrimination with Quantum Complete Graph Neural Network](https://mlsanity.com/paper/2403.04990/) (2024)
- [KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging](https://mlsanity.com/paper/2512.07420/) (2025)
- [Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems](https://mlsanity.com/paper/2507.19205/) (2025)

## Abstract (arXiv)

Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extracts the features of graph, but embeds additional information obtained by clustering of k-means of different particle features. We construct Haarpooling from five different features: absolute energy log E, transverse momentum log pT, relative coordinates (Deltaη,Deltaφ), the mixed ones (log E, log pT) and (log E, log pT, Deltaη,Deltaφ). The results show that an appropriate selection of information for HaarPooling enhances the accuracy of quark-gluon tagging, as adding extra information of log PT to the HMPNet outperforms all the others, whereas adding relative coordinates information (Deltaη,Deltaφ) is not very effective. This implies that by adding effective particle features from HaarPooling can achieve much better results than solely pure message passing neutral network (MPNN) can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally we compare the HMPNet study, ordering by pT, with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.