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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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History-dependent percolation on multiplex networks.

Ming Li1, Linyuan Lü2, Youjin Deng3

  • 1Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei 230026, China.

National Science Review
|October 25, 2021
PubMed
Summary

This study introduces a unified framework for understanding percolation transitions in interacting networks. The research reveals how system history influences transitions, with significant changes observed at infinite generations, offering insights into network structures.

Keywords:
brain networkscritical phenomenamultiplex networkspercolation

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Area of Science:

  • Complex systems science
  • Network science
  • Statistical physics

Background:

  • Interconnected systems and dynamics are widely studied across disciplines.
  • Existing models often lack a comprehensive understanding of connections between different percolation transitions.
  • Percolation theory is crucial for understanding network behavior and phase transitions.

Purpose of the Study:

  • To propose a unified framework for analyzing discontinuous percolation transitions in interacting networks.
  • To investigate the history-dependent nature of percolation processes.
  • To explore the impact of generations on transition dynamics.

Main Methods:

  • Development of a novel, history-dependent percolation model.
  • Theoretical analysis of the model's behavior across generations.
  • Monte Carlo simulations to validate theoretical findings.

Main Results:

  • The percolation transition nature is consistent at finite generations.
  • An abrupt change in transition behavior is observed at infinite generations.
  • The model demonstrates applicability to real-world network analysis.

Conclusions:

  • The proposed framework unifies the study of percolation transitions in interacting networks.
  • System history significantly impacts percolation dynamics, especially in the long-term (infinite generations).
  • The model offers a general method for network structure exploration and applications like detecting abnormalities in human brain networks.