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Detecting hidden layers from spreading dynamics on complex networks.

Łukasz G Gajewski1, Jan Chołoniewski1, Mateusz Wilinski2

  • 1Center of Excellence for Complex Systems Research, Faculty of Physics, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland.

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Summary

This study introduces methods to identify hidden layers in networks by analyzing cascade data from spreading processes. The approach accurately reconstructs unobserved spreading paths, improving data reliability.

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Area of Science:

  • Network Science
  • Data Reliability
  • Epidemiology

Background:

  • Spreading processes on networks require reliable data and identification of unobserved paths.
  • Understanding network structure is crucial for analyzing contagion dynamics.

Purpose of the Study:

  • To develop methods for hidden layer identification and reconstruction in multilayer networks.
  • To explore the relationship between task difficulty and network structure in spreading processes.

Main Methods:

  • Derivation of an exact likelihood expression for cascades in the susceptible-infected model.
  • Utilizing statistical properties of unimodal distributions and joint likelihood heuristics.
  • Analysis of both synthetic and real-world network data.

Main Results:

  • Successful estimation of hidden layer existence and content.
  • Success rates significantly outperform null models.
  • Demonstrated viability on diverse network types.

Conclusions:

  • The proposed methods effectively identify and reconstruct hidden layers in spreading processes.
  • Network structure influences the difficulty of identifying hidden layers.
  • The approach enhances data reliability by revealing unobserved spreading dynamics.