# GraphMuse: A Library for Symbolic Music Graph Processing

arXiv: https://arxiv.org/abs/2407.12671 · HTML: https://mlsanity.com/paper/2407.12671/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019)
- [Sampling methods for efficient training of graph convolutional networks: A survey](https://mlsanity.com/paper/2103.05872/) (2021)
- [Symphony Generation with Permutation Invariant Language Model](https://mlsanity.com/paper/2205.05448/) (2022)
- [miditok: A Python package for MIDI file tokenization](https://mlsanity.com/paper/2310.17202/) (2023)
- [Partitura: A Python Package for Symbolic Music Processing](https://mlsanity.com/paper/2206.01071/) (2022)
- [Symbolic Music Representations for Classification Tasks: A Systematic Evaluation](https://mlsanity.com/paper/2309.02567/) (2023)
- [Cadence Detection in Symbolic Classical Music using Graph Neural Networks](https://mlsanity.com/paper/2208.14819/) (2022)
- [PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation](https://mlsanity.com/paper/2107.14009/) (2021)
- [Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features](https://mlsanity.com/paper/2307.03544/) (2023)
- [Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem](https://mlsanity.com/paper/2304.14848/) (2023)
- [Graph-based Polyphonic Multitrack Music Generation](https://mlsanity.com/paper/2307.14928/) (2023)

## Built on by (most cited first, 5 of 5)

- [AnalysisGNN: Unified Music Analysis with Graph Neural Networks](https://mlsanity.com/paper/2509.06654/) (2025)
- [EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engraving](https://mlsanity.com/paper/2509.19412/) (2025)
- [MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models](https://mlsanity.com/paper/2509.26521/) (2025)
- [Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music](https://mlsanity.com/paper/2603.26478/) (2026)
- [From Prediction to Collaboration: Interactive Symbolic Music Analysis](https://mlsanity.com/paper/2607.13587/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have recently gained traction in symbolic music tasks, yet a lack of a unified framework impedes progress. Addressing this gap, we present GraphMuse, a graph processing framework and library that facilitates efficient music graph processing and GNN training for symbolic music tasks. Central to our contribution is a new neighbor sampling technique specifically targeted toward meaningful behavior in musical scores. Additionally, GraphMuse integrates hierarchical modeling elements that augment the expressivity and capabilities of graph networks for musical tasks. Experiments with two specific musical prediction tasks -- pitch spelling and cadence detection -- demonstrate significant performance improvement over previous methods. Our hope is that GraphMuse will lead to a boost in, and standardization of, symbolic music processing based on graph representations. The library is available at https://github.com/manoskary/graphmuse