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Fast fixation with a generic network structure.

Gareth J Baxter1, Richard A Blythe, Alan J McKane

  • 1Department of Physics & I3N, University of Aveiro, 3810-193 Aveiro, Portugal.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2012
PubMed
Summary
This summary is machine-generated.

This study reveals that network asymmetry, not just structure, impacts fixation time in stochastic copying models. Varying copying likelihood accelerates fixation, even in uniform networks.

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Area of Science:

  • Complex systems
  • Mathematical modeling
  • Network dynamics

Background:

  • Stochastic copying processes are fundamental to various scientific fields, including population genetics, ecology, linguistics, and opinion dynamics.
  • These models often exhibit absorbing fixation states, where all network nodes adopt a single state.
  • Previous research linked the mean fixation time to network size via degree distribution variations.

Purpose of the Study:

  • To explore how network asymmetry influences fixation time in stochastic copying processes.
  • To demonstrate that altering copying dynamics can affect fixation time independently of degree distribution.
  • To investigate the interplay between network degree distribution and asymmetry.

Main Methods:

  • Analysis of a broad class of stochastic copying models on networks.
  • Examination of models including Wright-Fisher, Hubbell-type, utterance selection, and voter models.
  • Varying network asymmetry (copying likelihood) while keeping degree distribution constant.

Main Results:

  • Mean fixation time can be accelerated by increasing asymmetry in copying dynamics.
  • This acceleration occurs even on homogeneous networks where certain nodes are preferentially copied.
  • A complex interplay exists between degree distribution and asymmetry, with robust results to network correlations.

Conclusions:

  • Network asymmetry is a critical factor, alongside degree distribution, in determining fixation times.
  • Preferential copying can significantly speed up fixation processes in networked systems.
  • The findings offer a more nuanced understanding of evolutionary and social dynamics on networks.