Realistic models for diffusion of innovation

It is widely acknowledged in the socio-economic literature that innovators and early adopters play an essential role in the diffusion of innovation. However, current approaches to influence maximization primarily concentrate on identifying influencers as the main targets of marketing campaigns. This perspective overlooks the fact that influencers and innovators seldom coincide, rendering the focus of influence maximization models misplaced. Practitioners often do not recognize that the influence maximization problem depends not only on the network structure but also on the underlying diffusion model. In this paper, we develop more realistic variants of the well-known linear threshold and independent cascade models. We incorporate the influence of adopter groups, which has a significant impact on the spread of influence. We leverage data from two different social networks and assess the efficiency of various centrality measures in the influence maximization model using both the traditional diffusion mechanisms and our novel approaches. The rankings obtained from the different models exhibit significant discrepancies implying that heuristics that perform well in a classical model may perform poorly in a more realistic setting. Instead of solely focusing on developing new algorithms for influence maximization, greater attention should be given to understanding and calibrating diffusion models to realistic settings.