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The outbreak of infectious diseases is a global public health threat for the international community. Modelling the propagation of epidemics in a society is one of the important fields in epidemiology science. It is essential to know the number of infected cases for estimating and controlling the spread of disease in the affected countries. In this study, we used complex network theory to model the spread mechanism of epidemic disease in social networks. We modeled a social complex network by graph theory. Individuals are considered as nodes and acquaintances between them are considered as links. Disease virus can transmit along the links between nodes (people) according to different situations. In this work, we proposed a dynamic model for simulating the outbreak of infectious disease on a social network based on the susceptible, exposed, infected and recovered (SEIR) dynamical categories. It has been tried to study the heterogeneity on the network by considering two key factors in the epidemic propagation: 1) The communications weights between individuals in the network 2) different body resistances of people based on age. The proposed dynamic model was applied on a real social network which was constructed in our previous research. We compared the proposed model with two different dynamic models. Finally, the simulations were compared with the reported data of infected cases of SARS outbreak in Hong Kong in 2003. The results indicated some similarity between our proposed model and the real reported data. Based on the results, it could be concluded that considering communications weights and body resistances of people captures the dynamic of disease spread in a proper way.