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Transitivity reinforcement in the coevolving voter model.

Nishant Malik1, Feng Shi2, Hsuan-Wei Lee3

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This study shows that reinforcing triangle closure in coevolving networks alters contagion dynamics. Models maintaining network clustering provide a more realistic approach to studying adaptive network systems.

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Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Triangle closure, a fundamental network property, influences contagion dynamics.
  • Existing coevolving network models often randomize transitivity, limiting their applicability.
  • A modified coevolving voter model is needed to maintain network clustering.

Purpose of the Study:

  • To investigate the impact of reinforced triangle closure on coevolving voter model dynamics.
  • To explore how maintaining network clustering affects contagion and dynamical states.
  • To develop a semi-analytical framework for predicting model behaviors.

Main Methods:

  • Numerical simulations of a modified coevolving voter model.
  • Explicit reinforcement and maintenance of triangle closure.
  • Semi-analytical framework using approximate master equations.

Main Results:

  • Reinforcing transitivity alters transitions and dynamical states in coevolving networks.
  • The presence of clustering significantly impacts network dynamics compared to randomized models.
  • The semi-analytical framework accurately predicts model behaviors across parameter settings.

Conclusions:

  • Models that maintain network clustering, unlike those that randomize it, offer a more realistic representation of adaptive network systems.
  • Reinforced transitivity fundamentally changes the dynamics observed in coevolving voter models.
  • The developed framework aids in understanding and predicting complex network behaviors.