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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Network Spreading from Network Dimension.

Jack Murdoch Moore1,2, Michael Small3,4, Gang Yan1,2

  • 1MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physical Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China.

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Summary
This summary is machine-generated.

This study introduces a new network correlation dimension to improve network spreading models. This method offers more accurate early-stage predictions for processes like disease transmission compared to complex existing models.

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Area of Science:

  • Network science
  • Epidemiology
  • Complex systems

Background:

  • Continuous-state network spreading models are vital for understanding transmission processes.
  • Existing models often fail due to ignoring global network correlations, leading to prediction errors.

Purpose of the Study:

  • To propose a novel network property, the network correlation dimension, to enhance spreading models.
  • To develop a more accurate and less complex spreading model for empirical networks.

Main Methods:

  • Characterizing network structure using the network correlation dimension.
  • Applying this dimension to susceptible-infected-recovered (SIR) spreading processes.
  • Comparing the new model's predictive accuracy against established models.

Main Results:

  • The proposed model provides more accurate predictions of early-stage spreading than complex existing models.
  • The network correlation dimension effectively captures global network properties relevant to spreading.
  • The model's basic reproduction number offers insights into the final state of the spreading process.

Conclusions:

  • Network correlation dimension is a powerful tool for improving network spreading models.
  • This approach offers a simpler yet more accurate alternative for analyzing epidemic dynamics and other network phenomena.