Study On The Problem Of Influence Maximizing Of Social Networks Based On Moth Flame Optimization

Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model.The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance.Although the greedy-based method can provide a reliable and accurate solution,it usually needs to traverse all nodes in the network when selecting influential nodes in each round.but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks.Meanwhile,although the centrality method based on the network structure has a high recognition efficiency,the recognition accuracy is low due to the failure to closely integrate with the propagation model.In this paper,we study the problem of Inflfluence maximization of social networks based on swarm intelligence algorithms,and discuss the following three research contents in detail.First,an improved moth flame optimization algorithm was proposed based on diversity and mutation strategies;then,we established a node influence evaluation model based on the network topology,and developed a discrete moth flame optimization method to search for the most influential node set;Finally,identification of influential users for influence maximization and cost minimization using a multi-objective discrete moth flame optimization.The specific work is summarized as follows:Moth-flame optimization algorithm based on diversity and mutation strategy.An improved moth-flame optimization algorithm is proposed to alleviate the problems of premature convergence and convergence to local minima.From the perspective of diversity,an inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm’s exploitation and global search abilities.Furthermore,a small probability mutation after the position update stage is added to improve the optimization performance.The performance of the proposed algorithm is extensively evaluated on a suite of CEC’2014 series benchmark functions and four constrained engineering optimization problems.The results of the proposed algorithm are compared with the ones of other improved algorithms presented in literatures.It is observed that the proposed method has a superior performance to improve the convergence ability of the algorithm.In addition,the proposed algorithm assists in escaping the local minima.Social network influence maximization algorithm based on discrete moth flame optimization.In order to solve the problem of low computational efficiency of traditional greedy algorithms.Here we establishes an effective influence assessment model based on the total valuation of neighbor nodes and valuation variance,motivated by the possibility of unreliable communication channels.We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets,using local crossover and mutation evolution scheme atop the canonical moth position updates.To accelerate convergence,a search area selection scheme derived from a degree-based heuristic is used.Experimental results on five real-world social networks,comparing our proposed method against several alternatives in current literature,indicates our approach to be effective and robust in tackling the influence maximization problem.Identification of influential users for influence maximization and cost minimization using a multi-objective discrete moth flame optimization.Some traditional models used to solve the influence maximization problem(IM)only consider the maximum propagation range that the seed node set can reach but ignore the cost difference between the potential candidate nodes.This is not characteristic of real-world network behaviour.To this end,this paper proposes a multi-objective optimization model based on the Influence spread Maximize and Costs Minimize(IM-CM).On the basis of DMMFO,using non-dominated sorting and crowding distance operator combined with DMFO,an improved non-dominated sorting moth flame optimization(INS-MFO)design method for IM-CM problem is proposed.By considering three types of real-world social networks(including two weighted undirected networks,three unweighted undirected networks and two unweighted directed networks).This paper show that the proposed method is able to generate a set of well-distributed preto optimal solutions and can provide operators useful information,such as trade-offff decisions,when promotiong new products.