Can one hear the position of nodes?
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 10 Nov 2022.
Builds on
10 of 36 references.
References · 10
- 2020Fast Differentiable Sorting and Rankingcited by 196
- 2017Cooperative Game Theory Approaches for Network Partitioningcited by 134
- 2016Very Deep Convolutional Neural Networks for Raw Waveformscited by 68
- 2018NetLSD: Hearing the Shape of a Graphcited by 51
- 2019A Comparative Study on End-to-end Speech to Text Translationcited by 40
- 2019vGraph: A Generative Model for Joint Community Detection and Node Representation Learningcited by 26
- 2019Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approachcited by 16
- 2018Machine Learning in Network Centrality Measures: Tutorial and Outlookcited by 7
- 2019RiWalk: Fast Structural Node Embedding via Role Identificationcited by 7
- 2020Approximating Network Centrality Measures Using Node Embedding and Machine Learningcited by 6
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