In this paper, we target the location promotion problem in location-based social networks (LBSNs). The location promotion problem is given a location, we select a set of users as seeds to influence as many users as possible who are likely to visit a selected location. Specifically, we model the location promotion problem as an influence maximization problem on a graph and explore the independent cascading diffusion model on the graph. To determine the propagation probability of the edges of our proposed graph, the relation between users and the selected location should be detected. A property of LBSN is that the major reason of users visiting a location is based on their mobility. Therefore, we propose a mobility model DMM (Distance-based Mobility Model) to model each user's mobility. DMM exploits random walk with restart and the power law property of users' movements. Based on DMM and the selected location, the propagation probability of edges can be derived. In the evaluation, we show the performance of our proposed algorithms on two real datasets.