Rresearch On Influence Maximization In Social Networks Based On Multi-verse Optimizer

Social network has become an important platform for information dissemination with the rapid development of Internet technology,people disseminate information and share knowledge through social network.The individuals in the network receive certain ideas,which will affect the surrounding individuals in the process of interaction thus changing their behavior and cognition,which in turn causes changes in the network topology.Therefore,influence analysis is of great significance for understanding the behavior characteristics of network nodes and analyzing the evolution of control network topology.As one of the important research contents of influence analysis,influence maximization aims to select a set of nodes from a given network to maximize the influence spread range under a specific propagation model.The study of the influence maximization problem has important research significance and application value for realizing practical activities such as viral marketing and rumor monitoring.To address the problems of high time complexity,low solution accuracy and poor scalability of traditional influence maximization algorithms,this paper adopts a meta-heuristic algorithm to solve the problem.However,the meta-heuristic algorithm has the problems of easily falling into local optimum,decreasing diversity in the late iteration and needing further improvement of solution accuracy based on different search strategies in discrete network space.Therefore,based on multi verse algorithm and multi-objective optimization theory,this paper studies the influence maximization problem of social networks.The specific work is outlined as follows:(1)To address the problems of meta-heuristic algorithms such as the high probability of entrapment in local optima,low accuracy of the solution,and decreasing diversity in the late iteration.The Double Clusters Multi-Verse Optimizer(DCMVO)algorithm is proposed as a solution to the problem of influence maximization.In DCMVO,based on the fact that nodes in the social network are susceptible to the influence of neighboring nodes,individuals in the globular cluster are updated using neighboring nodes with high similarity,which enhances local exploitation and improves the accuracy of the solution.To improve the global exploration,using a comprehensive learning strategy in the open cluster enables individuals to learn from surrounding individuals by dimensions,thereby expanding the search space.The wormhole mechanism is used to enhance the information interaction between double clusters during the iterative process,which serves to balance local exploitation and global exploration.Under the independent cascade(IC)model,extensive experiments conducted on seven actual social networks demonstrate the effectiveness of DCMVO.(2)Advertisement over social networks is critical for many enterprises,and spending less budget to achieve greater the spread of influence is regarded as an important issue in the field of social network.Most researchers only focus on how to select node sets to maximize influence,ignoring the goal of minimizing budget.Thus,a multi-objective function is defined based on the comprehensive consideration of two objectives: the influence maximization problem of budget minimization.While selecting the node set to maximize the influence spread,the calculation index of the number of fans is introduced to calculate the cost budget required by the node set to minimize the budget.Discrete Multi-Objective Multiverse Optimizer(DMOMVO)was proposed to solve the multi-objective problem effectively.In DMOMVO,a new encoding mechanism and discrete evolution rules are constructed.To address the problem of falling into local optimum,the monitoring strategy is used.Under the independent cascade model,the experimental analysis of three real networks shows that the proposed algorithm can get better solutions in complex cases.