Information propagation in stochastic networks

In this paper, a network-based stochastic information propagation model is developed. The information flow is modeled by a probabilistic differential equation system. The numerical solution of these equations leads to the expected number of informed nodes as a function of time and reveals the relationship between the degrees of the nodes and their reception time. The validity of the model is justified by Monte Carlo network simulation through the analysis of information propagation in scale-free and Erdős–Rényi networks. It has been found that the developed model provides more accurate results compared to the widely used network-based SI mean-field model, especially in sparse networks. •New temporal information propagation model for random networks.•Improved accuracy compared to the network-based SI mean-field model.•Numerical solution of the governing differential equation system.•Monte Carlo network simulation justifies the model.•Implemented simulation software is publicly available.