Homophilic networks evolving by mimesis

TL;DR

This paper introduces a mathematical model for homophilic social networks evolving through mutual imitation, demonstrating their tendency to segregate and establishing links with the Wolfram model of fundamental physics.

Contribution

The paper presents a novel mathematical framework for understanding how homophily and mimesis drive social network segregation, connecting it to fundamental physics models.

Findings

Networks tend to converge to segregated states with maximal in-group interactions.

Homophilic networks show no out-group information flow in the long run.

Connections are established between social network evolution and the Wolfram model.

Abstract

We provide a mathematical model for networks based on similarities (homophily) and evolving by mutual imitation (mimesis). We show that such social networks will converge to a state of segregation, where the in-group interactions will be maximal and there will be no out-group flow of information. We establish some connections between our model and the Wolfram model for fundamental physics.