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PreprintCC BY 4.0arXiv

A dual network approach to connect structure and flow in random networks

  • Marco Dentz
  • Philippe Gouze
  • Tanguy le Borgne
  • Alexandre Puyguiraud
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Abstract

Flows in random networks are known to exhibit heavy-tailed statistics, giving rise to anomalous transport in a range of biological, environmental and engineered systems. Yet, how structure determines flow remains an open question. Here we derive a dual network approach that relates flow statistics and network properties. Conditional statistics in a range of networks reveal the existence of two interlaced subnetworks with distinct hydraulic behaviors. Based on this hidden structure, we derive a universal analytical approach that predicts heavy-tailed flow statistics based on network topology and disorder distribution across a range of random networks.

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Cite

BibTeXRIS
BibTeX
@misc{dentz2026dual,
  title = {{A dual network approach to connect structure and flow in random networks}},
  author = {Marco Dentz and Philippe Gouze and Tanguy le Borgne and Alexandre Puyguiraud},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.05502},
  eprint = {2609.05502},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.05502},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - A dual network approach to connect structure and flow in random networks
AU  - Marco Dentz
AU  - Philippe Gouze
AU  - Tanguy le Borgne
AU  - Alexandre Puyguiraud
PY  - 2026
DA  - 2026/09/01
PB  - arXiv
DO  - 10.48550/arxiv.2609.05502
UR  - https://arxiv.org/abs/2609.05502
AB  - Flows in random networks are known to exhibit heavy-tailed statistics, giving rise to anomalous transport in a range of biological, environmental and engineered systems. Yet, how structure determines flow remains an open question. Here we derive a dual network approach that relates flow statistics and network properties. Conditional statistics in a range of networks reveal the existence of two interlaced subnetworks with distinct hydraulic behaviors. Based on this hidden structure, we derive a universal analytical approach that predicts heavy-tailed flow statistics based on network topology and disorder distribution across a range of random networks.
ER  -