Impact of social neighborhood on diffusion of innovation S-curve

Agent-based modeling (ABM) of Diffusion of Innovation (DOI) allows capturing of complex system phenomena that are related to social network topology, in contrast to traditional approaches such as Fisher-Pry or Bass models. These effects can be crucial for accurate prediction of DOI in the markets with strong influence of word-of-mouth. In this paper we compared DOI through random and scale-free social networks using ABM. The model predicts faster product adoption for a random network compared with a scale-free network with the same number of nodes due to the presence of hubs. Longer diffusion time in scale-free networks is related to lower information equality. Real world social networks can be a mixture of the two considered extreme cases and also can depend on the type of product.