Philosophers and social scientists have recently turned to Lewis sender–receiver games to provide an account of how lexical terms can acquire meaning through an evolutionary process. However, the evolution of meaning is contingent on both the particular sender–receiver game played and the choice of evolutionary dynamic. In this paper I explore some differences between models that presume an infinitely large and randomly mixed population and models in which a finite number of agents communicate with their neighbors in a social network. My results show that communication with neighbors is more conducive to the evolution of meaning than communication with strangers. Additionally, I show that the behavior of the system is highly dependent on the topological structure of the social network. I argue that a specific class of networks—small world graphs—is especially conducive to the evolution of meaning. This is because small world graphs have a short characteristic path length while still maintaining a high degree of correlation between neighbors. Since many actual social networks, such as friendship networks and nervous systems, are conjectured to be small world structures, these results indicate that these networks are quite hospitable to the efficient evolution of meaning.