  Human contact networks are constituted by a multitude of individuals and
pairwise contacts among them. However, the dynamic nature, which generates the
evolution of human contact networks, of contact patterns is not known yet.
Here, we analyse three empirical datasets and identify two crucial mechanisms
of the evolution of temporal human contact networks, i.e. the activity state
transition laws for an individual to be socially active, and the contact
establishment mechanism that active individuals adopt. We consider both of the
two 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 four
corresponding temporal network models, and compare the full prevalence time of
SI processes with various infection rates on the networks. The full prevalence
time of SI processes in the MD model is the same as that in real-world human
contact networks. Moreover, we find that the individual activity state
transition promotes the spreading process, while, the contact establishment of
active individuals suppress the prevalence. Apart from this, we observe that
even a small percentage of individuals to explore new social ties is able to
induce an explosive spreading on networks. The proposed temporal network
framework could help the further study of dynamic processes in temporal human
contact networks, and offer new insights to predict and control the diffusion
processes on networks.
