Abstract: The influence maximization problem in social network is to find top-k influential nodes in graph that maximize the number of influenced nodes．Some basic propagation models have been proposed to solve the influence maximization problem．But those models do not consider the relativity and importance of the node which we consider as an important measurement of influence．Thus,we propose a new PageRank-based propagation model,and employ the Greedy Algorithm to solve the influence maximization problem．The experimental results show that our proposed model is more effective than traditional Linear Threshold Model,Weighted Cascade Model and Independent Cascade Model in solving the influence maximization problem．

Key words: Social network,Influence maximization,PageRank,Information propagation models and algorithm