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Homophily, influence and the decay of segregation in self-organizing networks

Published online by Cambridge University Press:  23 March 2016

ADAM DOUGLAS HENRY ,
DIETER MITSCHE  and
PAWEŁ PRAŁAT
ADAM DOUGLAS HENRY
Affiliation:
School of Government and Public Policy, University of Arizona, Tucson, AZ, USA (e-mail: adhenry@email.arizona.edu)
DIETER MITSCHE
Affiliation:
Université de Nice Sophia-Antipolis, Laboratoire J-A Dieudonné, Parc Valrose, 06108 Nice cedex 02, France (e-mail: dmitsche@unice.fr)
PAWEŁ PRAŁAT
Affiliation:
Department of Mathematics, Ryerson University, Toronto, ON, Canada (e-mail: pralat@ryerson.ca)
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Abstract

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We study the persistence of network segregation in networks characterized by the co-evolution of vertex attributes and link structures, in particular where individual vertices form linkages on the basis of similarity with other network vertices (homophily), and where vertex attributes diffuse across linkages, making connected vertices more similar over time (influence). A general mathematical model of these processes is used to examine the relative influence of homophily and influence in the maintenance and decay of network segregation in self-organizing networks. While prior work has shown that homophily is capable of producing strong network segregation when attributes are fixed, we show that adding even minute levels of influence is sufficient to overcome the tendency towards segregation even in the presence of relatively strong homophily processes. This result is proven mathematically for all large networks and illustrated through a series of computational simulations that account for additional network evolution processes. This research contributes to a better theoretical understanding of the conditions under which network segregation and related phenomenon—such as community structure—may emerge, which has implications for the design of interventions that may promote more efficient network structures.

Keywords

network dynamicsnetwork segregationcommunity structurehomophilyinfluencegraph theorydifferential equation methodagent-based models
Type
Research Article
Information
Network Science , Volume 4 , Issue 1 , March 2016 , pp. 81 - 116
Copyright
Copyright © Cambridge University Press 2016 

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