Irregular cellular automata based diffusion model for influence maximization

Due to great communication among users in social networks, a lot of attention is paid to the spreading of information. This issue is of a huge consideration in modern viral marketing either. So far, different models have been proposed in many of which active and inactive users are cooperating in the simple form. Since the influence of individuals in spreading of information happens differently in the real world, in this article we propose a multi-state model for information spread based on cellular automata. We used different states for the proposed model as well as various levels of influence from the beginning up to the end. As an evaluation, proposed model not only has been examined with standard data corresponding to different social networks, but also has been compared with different thresholds. The results of simulations show the superiority of proposed model in comparison with linear threshold model.