Evolution prediction of multi-scale information diffusion dynamics

Information diffusion has become ubiquitous in various complex networks recently. Despite understanding information diffusion process being of utmost important to optimize network structure and regulate network function, the propagation mechanism of information diffusion is essentially unclear. Here we focus on the evolution prediction of information diffusion process. A problem typically encountered when exploiting traditional diffusion prediction methods is the limitedness of information available on diffusion graph, which hinders them from widespread use. This paper develops a novel prediction method, multi-scale diffusion prediction (MScaleDP). MScaleDP aggregates microscopic spreading modules of individual nodes using a unidirectional label propagation algorithm for macroscopic diffusion prediction, in which the label selection mechanism corresponds to the microscopic spreading decision-making. Through microscopic spreading behavior modeling, the underlying influential factors and the principal driving mechanisms of diffusion process are identified. Moreover, we find that the accuracy of spreading behavior estimation does not always increase with the growth of the feature number. We also find that the accuracy of spreading behavior estimation is not strongly correlated with the estimation model when sufficient features are considered. Our method is successfully tested on microblogging network, and represents a valuable tool for gaining insights on information diffusion process.