Language is a complex dynamical system that is shaped not just through biological
evolution but by the way it is used in a social context. Sociolinguists have
long understood that the structure of a society strongly affects the nature of the
languages that emerge. Computational models of language evolution, however,
generally neglect the effect of social structure by modelling extremely simple
population dynamics. This study explores the coevolution of language and social
structure using a simple, abstract model of language learning and a plausible
mechanism for network growth, namely homophily. Evolved networks are
found to possess the characteristic measures of social networks: assortative mixing,
transitivity and prominent community structure. The effect of embedding
language-learners in the network is found to be significant. This model may
also provide a platform on which existing theories and computational models of
language evolution can be evaluated
