A network model of conviction-driven social segregation
Gianluca Teza, Samir Suweis, Marco Gherardi, Amos Maritan, and Marco, Cosentino Lagomarsino

TL;DR
This paper introduces a network model demonstrating that social segregation can occur without spatial factors, driven by conviction-based social connections and popularity, revealing a phase transition between mixed and segregated states.
Contribution
The study presents a novel network model showing how conviction-driven social segregation can happen in well-mixed networks without spatial influence, highlighting a phase transition mechanism.
Findings
Segregation occurs via a phase transition controlled by a 'tolerance' parameter.
Minority groups tend to segregate faster than majorities.
Segregation can arise solely from conviction-based rewiring, independent of spatial factors.
Abstract
In order 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 trade-off 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…
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