  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 Erdos-Renyi 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.
