Abstract：Information diffusion blocking maximization is used to select and delete the best l nodes (edges) to minimize the number of nodes receiving information in the network. However, the model does not take into account the node's influence which blocks the information flow and lowers the efficiency. This paper presents an information diffusion blocking model that considers the node's influence with a method based on the sampling average approximation (SAA). The model is selects and deletes the best l nodes to change the network structure which minimizing the influence of the target nodes. The model is a stochastic optimization problem which is transferred into a deterministic problem using SAA. The problem is then encoded as a mixed integer programming (MIP) problem. Finally, a quantum genetic algorithm is used to select the best l nodes and remove them. Simulations show that the best l nodes selected by this model influence the information diffusion over a smaller range and the processing time is shorter than the traditional model.
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