On the clustering coefficients of temporal networks and epidemic dynamics

As a basic concept of complex network theory, clustering coefficient is vividly defined as the closeness of friend cliques in social networks. Now the availability of large-scale high-resolution empirical data allows researchers to study social networks with more details of time, and extend the static network topology with temporal dimension. Here we propose two definitions of temporal clustering coefficients, taking into account the consequence and the durations of link, respectively. With the verification of SIS (Susceptible-Infected-Susceptible) epidemic dynamics on three empirical temporal data sets, the Fudan Wi-Fi data set and two RFID human face-to-face data sets, we find that the epidemic threshold decreases with enhancing the temporal clustering of involved networks, while the one in the case of aggregated (static) networks can not present such dependence.