Can one hear the position of nodes?

TL;DR

This paper explores how sounds emitted by nodes in a network can reveal their position and centrality, using neural networks to interpret wave-forms and suggesting potential applications in arts and network visualization.

Contribution

It introduces a neural network approach to infer network centrality from node sounds, bridging spectral graph theory and acoustic analysis.

Findings

Neural network successfully predicts node centrality from sound data

Sounds emitted by nodes encode information about network topology

Potential applications in arts and alternative network visualization methods

Abstract

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.