The traditional information propagation model is more suitable for homogeneous network, and cannot be effectively applied to the non-homogeneous scale-free Social Network (SN). To solve this problem, an information propagation model based on local information was proposed. Topological characteristic difference between users and different effect on information propagation of user influence were considered in the model, and the probability of infection was calculated according to the neighbor nodes' infection and authority. Thus it could simulate the information propagation on real social network. By taking simulation experiments on Sina microblog networks, it shows that the proposed model can reflect the propagation scope and rapidity better than the traditional Susceptible-Infective-Recovered (SIR) model. By adjusting the parameters of the proposed model, it can verify the impact of control measures to the propagation results.

Influence maximization problem in micro-blog cannot be solved by simple user rank algorithm. To solve this problem, a greedy algorithm based on Extended Linear Threshold Model (ELTM) was proposed to solve Top-K problem in microblog. A concept of influence rate and a WIR (Weibo Influence Rank) algorithm were established to determine the user's influence by summarizing the key factors. Then, based on WIR values, an influence propagation model was proposed. After using greedy algorithm, the Top-K nodes were excavated. A simulation test based on Sina micro-blog was performed to validate the effectiveness of the proposed method. The result shows that the method outperforms the traditional algorithm.