Can one hear the position of nodes?
This work addresses network representation learning by introducing auralization as a novel method for analyzing network topology, potentially impacting arts and visualization.
The paper tackles the problem of inferring node centrality measures from wave propagation sounds in networks, achieving plausible sound recognition in most cases.
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.