  We propose GLAudio: Graph Learning on Audio representation of the node
features and the connectivity structure. This novel architecture propagates the
node features through the graph network according to the discrete wave equation
and then employs a sequence learning architecture to learn the target node
function from the audio wave signal. This leads to a new paradigm of learning
on graph-structured data, in which information propagation and information
processing are separated into two distinct steps. We theoretically characterize
the expressivity of our model, introducing the notion of the receptive field of
a vertex, and investigate our model's susceptibility to over-smoothing and
over-squashing both theoretically as well as experimentally on various graph
datasets.
