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Movement Retraining using Real-time Feedback of Performance
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Fitness-based network growth with dynamic feedback.

I E Smolyarenko1

  • 1Department of Mathematical Sciences, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 16, 2014
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Summary
This summary is machine-generated.

This study explores network growth models where node fitness influences new connections. We found that linear fitness-degree mapping results in power-law distributions, while nonlinear mapping leads to stretched exponential distributions.

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Area of Science:

  • Network Science
  • Statistical Physics
  • Complex Systems

Background:

  • Understanding network evolution is crucial for modeling real-world systems.
  • Node attractiveness (fitness) plays a significant role in network formation.
  • The relationship between node fitness and degree distribution is not fully understood.

Purpose of the Study:

  • To investigate network growth models incorporating a feedback mechanism between node fitness and degree distribution.
  • To analyze the impact of linear and nonlinear mappings between fitness and degree on the resulting network structure.
  • To determine the emergent degree distributions in these models.

Main Methods:

  • Development of a class of network growth models with fitness-based attachment.
  • Introduction of a feedback loop where new node fitness is drawn from the evolving degree distribution.
  • Mathematical analysis of fixed points for linear and nonlinear fitness-degree mapping scenarios.

Main Results:

  • In linear mapping cases, the emergent degree distribution follows a power-law asymptotically.
  • In nonlinear mapping cases, the degree distributions converge to a stretched exponential form.
  • The feedback mechanism is key to generating these distinct distribution types.

Conclusions:

  • The study provides insights into the emergence of different degree distributions in evolving networks based on fitness mechanisms.
  • The findings highlight the importance of the mapping function between node fitness and degree in determining network topology.
  • These models offer a framework for understanding phenomena in diverse complex systems, from social networks to biological systems.