mlsanityData to 13 Oct 2026

GraphMuse: A Library for Symbolic Music Graph Processing

arXiv 2407.12671 ↗First posted 17 Jul 2024cs.SD cs.AI cs.DLcited by 5

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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

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

18 of 32 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  2. 2017Inductive Representation Learning on Large Graphscited by 6,303
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,911
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,689
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,135
  6. 2019Heterogeneous Graph Attention Networkcited by 733
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  9. 2021Sampling methods for efficient training of graph convolutional networks: A surveycited by 40
  10. 2022Symphony Generation with Permutation Invariant Language Modelcited by 35
  11. 2023miditok: A Python package for MIDI file tokenizationcited by 27
  12. 2022Partitura: A Python Package for Symbolic Music Processingcited by 16
6 more
  1. 2023Symbolic Music Representations for Classification Tasks: A Systematic Evaluationcited by 14
  2. 2022Cadence Detection in Symbolic Classical Music using Graph Neural Networkscited by 11
  3. 2021PKSpell: Data-Driven Pitch Spelling and Key Signature Estimationcited by 7
  4. 2023Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Featurescited by 7
  5. 2023Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problemcited by 5
  6. 2023Graph-based Polyphonic Multitrack Music Generationcited by 2

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025AnalysisGNN: Unified Music Analysis with Graph Neural Networkscited by 2
  2. 2025EngravingGNN: A Hybrid Graph Neural Network for End-to-End Piano Score Engravingcited by 0
  3. 2025MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Modelscited by 0
  4. 2026Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Musiccited by 0
  5. 2026From Prediction to Collaboration: Interactive Symbolic Music Analysiscited by 0