  This work explores areas overlapping music, graph theory, and machine
learning. An embedding representation of a node, in a weighted undirected graph
$\mathcal{G}$, is a representation that captures the meaning of nodes in an
embedding space. In this work, 383 Bach chorales were compiled and represented
as a graph. Two application cases were investigated in this paper (i) learning
node embedding representation using \emph{Continuous Bag of Words (CBOW),
skip-gram}, and \emph{node2vec} algorithms, and (ii) learning node labels from
neighboring nodes based on a collective classification approach. The results of
this exploratory study ascertains many salient features of the graph-based
representation approach applicable to music applications.
