Social network analysis is concerned with the analysis of influence of an individual within a social network and how the influence diffuses through the network. It has been shown useful in business analytics. In this paper, we extend a nonlinear dynamical system that accurately models virus propagation in epidemiology to model information diffusion in social networks. Our approach can numerically calculate each node’s probability to get activated given the initial active set. It provides an alternative way of estimating the number of nodes reached by the initial target set in the diffusion process. We validate our approach by comparing its predicting performance with diffusion simulations. Using the number of nodes reached in the diffusion process as an influence measure, our results show that the proposed method can provide a way of identifying nontrivial nodes as influencer.