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Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches

M. Kapeller,Georg Jäger,M. Füllsack

Published 2019 in Computational Social Networks

ABSTRACT

In the standard situation of networked populations, link neighbours represent one of the main influences leading to social diffusion of behaviour. When distinct attributes coexist, not only the network structure, but also the distribution of these traits shape the typical neighbourhood of each individual. While assortativity refers to the formation of links between similar individuals inducing the network structure, here, we separate the formation of links from the actual distribution of an attribute on the topology. This is achieved by first generating different network types (e.g., lattice, scale free, and small world), followed by the procedure of distributing attributes. With this separation, we try to isolate the effect that attribute distribution has on network diffusion from the effect of the network structure itself. We compare random distributions, where behaviour types are highly mixed, and homophilic distributions, where similar individuals are very likely to be linked, and examine the effects on social contagion in a population of mainly reciprocal behaviour types. In addition, we gradually mix homophilic distribution, by random rewiring, adding links and relocating individuals. Our main results is that attribute distribution strongly influences collective behaviour and the actual effect depends on the network type. Under homophilic distribution the equilibrium collective behaviour of a population tends to be more divers, implying that random distributions are limited for illustration of collective behaviour. We find that our results are robust when we use different gradual mixing methods on homophilic distribution.

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