Effects of exogenous input on adoption rates in social networks

Modeling information diffusion is an often investigated area in social network science. Optimizing propagation through targeting interactions is an intriguing subfocus within this area, where the goal is to identify agents in the network best capable of diffusing information. Our work addresses a pragmatic context where identifying these agents is particularly useful: technology adoption. In this work, we model agent cognitive and social qualities as they are relevant to information communication. We show that a formulation of the social network using cognitively-rich nodes allows for the identification of agents to whom targeting for exogenous input provides for more optimal adoption rates. We then present how the representation falls under the theoretical guarantees of performance, as forwarded by Kempe et al. [12].