Identifying Influential Spreaders by Temporal Efficiency Centrality in Temporal Network

Identifying influential spreaders is an important issue for capturing the dynamics of information diffusion in temporal networks. Most of the identification of influential spreaders in previous researches were focused on analysing static networks, rarely highlighted on dynamics. However, those measures which are proposed for static topologies only, unable to faithfully capture the effect of temporal variations on the importance of nodes. In this paper, a shortest temporal path algorithm is proposed for calculating the minimum time that information interaction between nodes. This algorithm can effectively find out the shortest temporal path when considering the network integrity. On the basis of this, the temporal efficiency centrality (TEC) algorithm in temporal networks is proposed, which identify influential nodes by removing each node and taking the variation of the whole network into consideration at the same time. To evaluate the effectiveness of this algorithm, we conduct the experiment on four real-world temporal networks for Susceptible-Infected-Recovered (SIR) model. By employing the imprecision and the Kendall's au coefficient, The results show that this algorithm can effectively evaluate the importance of nodes in temporal networks.