Identification of important nodes in static and temporal networks

Ranking the spreading influence of nodes in static and temporal networks is of great importance in practice and research. The key to rank a node’s spreading ability is to evaluate the fraction of susceptible nodes been infected by the target node during the outbreak, i.e., the outbreak size. In our group, we present method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of Markov process, this method solves the problem of the nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node to be infected by its infected neighbours. We have employed the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes’ outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.