Research On Seed Node Set Optimization In Social Network Information Publishing

With the development of Internet technology,social networks have gradually become the main medium for interpersonal information exchange and a broad platform for information publishing.Because the information propagation process in social networks has the characteristics of higher speed and low cost.Therefore,the research on information publishing based on social networks is of great significance to the fields of social public opinion management and commercial marketing activities.It has attracted a lot of attention from academia and industry and has become a hot research field at home and abroad.Existing research is usually based on a specific information propagation model,using the network structure attributes of nodes in the social network and specific algorithms to solve the information publishing problem.The above solution provides a feasible way to solve the problem of information publishing,but there are still some shortcomings.For example,the topic attribute of the information to be published is not taken into consideration,and the resource consumption occurring during the information propagation process is not considered.The complexity of the above scheme is too high for solving a large social network.In this paper,the problem of information publishing based on social network is studied in view of the shortcomings of the above schemes.The main work of this paper includes the following parts:(1)In the process of simulating the information propagation,the topic attributes of the information to be published(target information)are taken into account,and the fitting accuracy of the model to the information propagation process and the final influence spread is improved.(2)In the process of constructing the optimization model,taking into account the resource consumption(propagation cost)occurring in the information propagation process,the information publishing problem is modeled as a multi-objective optimization model with the information influence spread and information propagation cost as the optimization objectives.Synchronize the information influence spread and information propagation cost.(3)This paper develops a multi-objective differential evolution algorithm that can quickly converge to the optimal solution for the complexity of social networks and the difficulty of solving the information publishing problem,which shortens the problem solving time and improves the scalability of the algorithm.(4)On the real social network data set,the verification scheme and optimization algorithm proposed in this paper are verified.The final experimental results show that the proposed solution can obtain the optimal solution with the minimum propagation cost and the maximum propagation range,and the solution proposed in this paper has lower time complexity.