Abstract: Understanding how communication networks form and evolve is a crucial research issue in complex network analysis. Various methods are proposed to explore networks generation and evolution mechanism. However, the previous methods usually pay more attention to macroscopic characteristics rather than microscopic characteristics, which may lead to lose much information of individual patterns. Since communication network is associated closely with user behaviours, the model of communication network also takes into consideration the individual patterns. By implicitly labeling each network node with a latent attribute-activity level, we introduce an efficent approach for the simulation and modeling of communication network based on topic model. We illustrate our model on a real-world email network obtained from email logs. Experimental results show that the synthetic network preserves some of the global characteristics and individual behaviour patterns. Besides, due to privacy policy and restricted permissions, it is arduous to collect a real large-scale communication network dataset in a short time. Much research work is constrained by the absence of real large-scale datasets. To address this problem, we can use this model to generate a large-scale synthetic communication network by a small amount of captured communication stream. Moreover, it has linear runtime complexity and can be paralleled easily.