In this paper, a network based stochastic information propagation model is developed. 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 and accuracy of the model are justified by Monte Carlo network simulations through the analysis of information propagation in scale-free and Erdos-Renyi  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 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.
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network based
network-based
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Information
The information
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and accuracy
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simulations
simulation
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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