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Network model of conviction-driven social segregation.

Gianluca Teza1, Samir Suweis1, Marco Gherardi2,3

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Social segregation can occur in well-mixed networks without spatial factors. A model shows that conviction and popularity drive network rewiring, leading to a phase transition between mixed and segregated states.

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Area of Science:

  • Social network analysis
  • Computational sociology
  • Statistical physics

Background:

  • Understanding social segregation is crucial for prediction and prevention.
  • Existing models often rely on spatial factors, leaving the role of social dynamics in well-mixed networks unclear.

Purpose of the Study:

  • To investigate the possibility and mechanisms of social segregation in networks where space is not a factor.
  • To model segregation based on discrete convictions and social connection preferences.

Main Methods:

  • Development and analysis of a simple model of segregation on networks.
  • Incorporation of individual conviction and popularity as criteria for social connections.
  • Numerical and analytical solutions to study the phase transition.

Main Results:

  • A phase transition exists between well-mixed and segregated states, determined by a tolerance parameter balancing conviction and popularity.
  • Minority convictions segregate more rapidly.
  • Aversion alone can trigger a segregation threshold.

Conclusions:

  • Social segregation can emerge in networks lacking spatial dimensions.
  • The interplay between conviction-based and popularity-based social rewiring drives segregation.
  • The findings offer a general principle for segregation transitions in non-spatial network dynamics.