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

翻译：图神经网络（GNNs）近期在符号音乐任务中受到关注，但统一框架的缺乏阻碍了研究进展。为填补这一空白，我们提出了GraphMuse——一个用于符号音乐任务的图处理框架与库，旨在促进高效的音乐图处理与GNN训练。本工作的核心贡献是一种针对乐谱中有意义行为专门设计的新型邻居采样技术。此外，GraphMuse集成了层次化建模组件，增强了图网络在音乐任务中的表达能力与功能。通过音高拼写与终止式检测两项具体音乐预测任务的实验，本方法较先前方案展现出显著的性能提升。我们希望GraphMuse能推动基于图表示的符号音乐处理研究的发展与标准化。该库已发布于 https://github.com/manoskary/graphmuse。