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Mobility pattern of device users plays a crucial role in a wide range of mobile computing applications, including data forwarding, content sharing, information search and advertising. Hence, it is important to characterize the mobility path information of users, so as to accurately predict user mobility. In this paper, we introduce two typical user mobility patterns: standard Markov and semi-Markov models. Especially, we experimentally explore the correlation of community and geography information in Mobile Social Networks (MSNets), and analyze user sojourn time distribution over communities. Both of theoretical analysis and trace-driven simulation results show that semi-Markov model is more effective in characterizing user mobility pattern and further making more accurate mobility prediction compared with standard Markov model.