Abstract: Influence spread is one of key problems about dynamic process problems on complex networks, and the research results of influence maximization problem based on dynamic networks are less. We discussed dynamic independent cascade model and dynamic linear threshold model and proposed a dynamic influence maximization problem based on above two models. I}hen, we presented an improved greedy algorithm, which eliminates the uncertainty of stochastic models and improves its performance by using connected graph approach. The algorithm was validated on four datasets with different sizes including AS, EMAII，DELICIOUS and DI3I_P. The results show that,the size of influence spread of our algorithm has an obvious advantage and time efficiency is better compared with H I} algorithm.

Key words: Dynamic network, Influence maximization, Information diffusion model, Heuristic algorithm