  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
