Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics

Epidemic models have long been used to study information diffusion in complex artificial, biological and social networks. Here, we propose a novel recurrent-state epidemic model for information diffusion in networked systems by introducing a potential-spreaders compartment into the susceptible–infected–recovered–susceptible (SIRS) model. Our model assumes that not all susceptible nodes are equally likely to become efficient information spreaders, and that information can be repeatedly disseminated in cycles, even after its temporary decay. We observed that the introduced potential spreaders compartment in our model enables a more convenient state-transition process and an accurate description of information diffusion based on discrete time. Specifically, we found that our analytic results are in good agreement with numerical simulations on both artificial systems and eight different types of real-world biological, medical, and social networks. Unlike susceptible or recovered nodes, we further observed that potential spreader nodes in our model can serve as a relatively good predictor of the peaks of contagion outbreaks. Monitoring the number of potential spreaders could thus be beneficial for predicting both infection transmission and information propagation in complex networks and for characterizing their temporal dynamical patterns. Our model could be applied to a wide variety of spreading phenomena with recurrent-state endemic dynamics, such as seasonal influenza, re-tweeting behavior, or re-emergence of rumors and fashion trends in online social networks.