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Related Experiment Video

Updated: Nov 29, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Hyperbolic mapping of human proximity networks.

Marco A Rodríguez-Flores1, Fragkiskos Papadopoulos2

  • 1Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, 3036, Cyprus.

Scientific Reports
|November 21, 2020
PubMed
Summary

Human proximity networks can be mapped to hyperbolic spaces, revealing community structures and aiding in predicting disease spread. This hyperbolic embedding offers new insights into human interaction dynamics.

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Area of Science:

  • Complex systems science
  • Network science
  • Epidemiology

Background:

  • Human proximity networks are crucial for understanding disease and information transmission.
  • These temporal networks are often sparse in individual snapshots.
  • Previous methods focused on traditional, non-temporal complex networks.

Purpose of the Study:

  • To map human proximity networks into hyperbolic spaces.
  • To investigate the utility of hyperbolic embeddings for analyzing temporal network properties.
  • To explore applications in community detection, routing, and epidemic prediction.

Main Methods:

  • Time-aggregated representation of human proximity networks.
  • Embedding aggregated networks into hyperbolic space using established complex network methods.
  • Theoretical justification and experimental validation across six real-world systems.

Main Results:

  • Successful hyperbolic embedding of time-aggregated human proximity networks.
  • Hyperbolic maps facilitate community identification and efficient greedy routing.
  • Hyperbolic distance predicts epidemic arrival times and future links with high precision.

Conclusions:

  • Hyperbolic embedding is a compatible and powerful method for analyzing human proximity networks.
  • This approach enhances understanding of network structure, dynamics, and epidemic spreading.
  • Hyperbolic mapping offers a novel perspective for predicting emergent behaviors in human systems.