无向超图模型下的一类影响力最大化问题算法研究

he budget influence maximization problem is the one of expansion of the influence maximization problem. At present, most studies mainly focus on finding high influence users in the social networks described in the figure. Aiming at the traditional problem of maximizing budget influence, which does not consider the diversified relationship and group influence between users, a problem of maximizing budget influence based on undirected hypergraph is proposed. The problem is to find highly influential users under a given budget in undirected hypergraph social networks. In this paper, an information diffusion model based on generalized linear threshold of undirected hypergraph is given. The problem has been proved to be NP-hard, and the objective function is non-submodularity. Budget greedy algorithm and budget greedy algorithm based on preprocessing are proposed to solve it.The experiments on two real online social network datasets verify the correctness and effectiveness of our methods.The results show that the budget greedy algorithm based on preprocessing has good performance advantages.