Information and Knowledge Diffusion Dynamics in Complex Networks with Independent Spreaders

Information and knowledge diffusion are important dynamical processes in complex social systems, in which the underlying topology of interactions among individuals is often modeled as networks. Recent studies have examined various information diffusion scenarios primarily focusing on the dynamics within one network; yet, relatively little scholarly attention has been paid to possible interactions among individuals beyond the focal network. Here, in this study, we account for this phenomenon by modeling the information diffusion dynamics with the involvement of independent spreaders in a susceptible–exposed–infectious–recovered contagion process. Independent spreaders receive information using latent information transmission pathways without following the links in the focal network and can spread the information to remote areas of the network not well connected to the major components. We derive the mathematics of the critical epidemic thresholds on homogeneous and heterogeneous networks as a function of the infectious rate, exposure rate, recovery rate and the activeness of independent spreaders. We present simulation results on Small World and Scale-Free complex networks, and real-world social networks of Facebook artists and physicist collaborations. The result shows that the extent to which information or knowledge can spread might be more extensive than we can explain in terms of link contagion only. In addition, these results also help to explain how the activeness of independent spreaders can affect the diffusion process of information and knowledge in complex networks, which may have implications for studies exploring other dynamical processes.