A Cellular Automaton based Network Diffusion model: Preparation for more scalable Viral Marketing

Abstract

Nowadays, social network web sites are becoming increasingly popular. Accordingly, viral marketing which aimed to promote products in social network received more and more attention. The Network Diffusion model describes how information or influence is propagated in a social network. It is of great importance to the viral marketing/influence maximization problem. However, there are two challenges: first, how to build the diffusion model so as to simulate the diffusion process better, and second, how improve the efficiency of the diffusion model so that it can be applied to more scalable viral marketing problems. To tackle these two problems, we proposed a Cellular Automaton based Network Diffusion (CAND) model. We also proved that the CAND model is equivalent to the Linear Threshold (LT) model under certain conditions. Moreover, the model is realized and a speed up method is used so as to improve the efficiency of the model. Experiments show that compared with the LT model, our model is much more efficient on four real life social network datasets, indicating a better scalability for Viral Marketing.