  Wave propagation through nodes and links of a network forms the basis of
spectral graph theory. Nevertheless, the sound emitted by nodes within the
resonating chamber formed by a network are not well studied. The sound emitted
by vibrations of individual nodes reflects the structure of the overall network
topology but also the location of the node within the network. In this article,
a sound recognition neural network is trained to infer centrality measures from
the nodes' wave-forms. In addition to advancing network representation
learning, sounds emitted by nodes are plausible in most cases. Auralization of
the network topology may open new directions in arts, competing with network
visualization.
