Network model of conviction-driven social segregation

Gianluca Teza, Samir Suweis, Marco Gherardi, Amos Maritan, and Marco Cosentino Lagomarsino
Phys. Rev. E 99, 032310 – Published 29 March 2019

Abstract

To measure, predict, and prevent social segregation, it is necessary to understand the factors that cause it. While in most available descriptions space plays an essential role, one outstanding question is whether and how this phenomenon is possible in a well-mixed social network. We define and solve a simple model of segregation on networks based on discrete convictions. In our model, space does not play a role, and individuals never change their conviction, but they may choose to connect socially to other individuals based on two criteria: sharing the same conviction and individual popularity (regardless of conviction). The tradeoff between these two moves defines a parameter, analogous to the “tolerance” parameter in classical models of spatial segregation. We show numerically and analytically that this parameter determines a true phase transition (somewhat reminiscent of phase separation in a binary mixture) between a well-mixed and a segregated state. Additionally, minority convictions segregate faster and inter-specific aversion alone may lead to a segregation threshold with similar properties. Together, our results highlight the general principle that a segregation transition is possible in absence of spatial degrees of freedom, provided that conviction-based rewiring occurs on the same time scale of popularity rewirings.

  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
3 More
  • Received 2 August 2018
  • Revised 1 February 2019

DOI:https://doi.org/10.1103/PhysRevE.99.032310

©2019 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsNetworksInterdisciplinary Physics

Authors & Affiliations

Gianluca Teza1,*, Samir Suweis1, Marco Gherardi2,3, Amos Maritan1, and Marco Cosentino Lagomarsino2,4,†

  • 1Dipartimento di Fisica e Astronomia G. Galilei, University of Padova, Via Marzolo 8, 35131 Padova, Italy
  • 2Dipartimento di Fisica, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy
  • 3Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
  • 4IFOM, FIRC Institute for Molecular Oncology, Milan, Italy

  • *gianluca.teza@phd.unipd.it
  • marco.cosentino-lagomarsino@ifom.eu

Article Text (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 99, Iss. 3 — March 2019

Reuse & Permissions
Access Options
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review E

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×