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Human contact networks are constituted by a multitude of individuals andpairwise contacts among them. However, the dynamic nature, which generates theevolution of human contact networks, of contact patterns is not known yet.Here, we analyse three empirical datasets and identify two crucial mechanismsof the evolution of temporal human contact networks, i.e. the activity statetransition laws for an individual to be socially active, and the contactestablishment mechanism that active individuals adopt. We consider both of thetwo mechanisms to propose a temporal network model, named the memory driven(MD) model, of human contact networks. Then we study the susceptible-infected(SI) spreading processes on empirical human contact networks and fourcorresponding temporal network models, and compare the full prevalence time ofSI processes with various infection rates on the networks. The full prevalencetime of SI processes in the MD model is the same as that in real-world humancontact networks. Moreover, we find that the individual activity statetransition promotes the spreading process, while, the contact establishment ofactive individuals suppress the prevalence. Apart from this, we observe thateven a small percentage of individuals to explore new social ties is able toinduce an explosive spreading on networks. The proposed temporal networkframework could help the further study of dynamic processes in temporal humancontact networks, and offer new insights to predict and control the diffusionprocesses on networks.

Evolving nature of human contact networks with its impact on epidemic processes