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Trade-offs between structural richness and perceptual robustness in music network representations

Lluc Bono Rosselló1,*, Robert Jankowski2,3,4, Hugues Bersini1, Marián Boguñá3,4, and M. Ángeles Serrano3,4,5

  • *Contact author: Lluc.Bono.Rossello@ulb.be
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Phys. Rev. Research 8, 033127 – Published 30 July, 2026

DOI: https://doi.org/10.1103/x6hc-jb2r

Abstract

Music is a structured and perceptually rich sequence of sounds in time, whose perception is shaped by the interplay of expectation and uncertainty about what comes next. Yet, the uncertainty we infer from music depends on how the musical piece is encoded as an event sequence. In this work, we use network representations, in which event types are nodes and observed transitions are directed edges, to compare how different feature encodings shape the transition structure we recover and how robust that structure is under modeled perceptual constraints. We systematically analyze eight encodings of piano music, from single-feature vocabularies to richer multifeature combinations. These representational choices reorganize the state space and fundamentally reshape network topology, shifting how uncertainty is distributed across transitions. To connect these descriptive differences to perception, we adopt a perceptual-constraint model that captures imperfect access to transition statistics. Overall, compressed single-feature representations yield dense transition structures with higher entropy rates, corresponding to higher average uncertainty per step, yet low model error, indicating that the constrained estimate stays close to the corpus transitions. In contrast, richer multifeature representations preserve finer distinctions but expand the state space, sharpen transition profiles, lower entropy rates, and increase model error. Finally, across representations, uncertainty concentrates in diffusion-central nodes while model error remains low there, suggesting an informational landscape in which predictable flow coexists with localized surprise. Overall, our results show that feature choice shapes not only the networks we reconstruct, but also which transition statistics are available and how vulnerable they are to distortion under perceptual constraints.

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Phys. Rev. Research 8, 033127– Published 30 July, 2026
  • Received 9 March 2026
  • Accepted 8 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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