The Maximum Influence Analysis Based On The Triangular Neighborhood In Complex Network

A complex network is a network that has some or all of its properties,such as small-world and scale-free.All areas of social life can be abstracted as complex networks.Social networks,biological networks,and international financial networks all have the characteristics of complex networks.Therefore,the study of complex networks has great theoretical value and practical significance to the real society.This article focuses on the two aspects of the importance of ordering and maximizing the influence of nodes in a complex network.The following two issues will be discussed.Firstly,an algorithm based on the importance of triangle neighborhood node and k-shell is proposed.This algorithm solves the problem of k-shell decomposition algorithm with coarse graining,so that nodes with different degrees of importance have the same k-shell value,and do not consider it.Problems such as overlapping effects when nodes propagate information.Aiming at the above problems,the similarity of the triangular neighborhood is introduced,and the local and global attributes of the node are considered at the same time in order to fully evaluate the importance of the node and improve the accuracy and effectiveness of the algorithm.Second,in order to make the information more quickly and more widely spread,an algorithm based on community-based hybrid maximization of impact is proposed.It solves the problems such as the high calculation cost of the traditional hill-climbing greedy algorithm and the low calculation accuracy of the heuristic algorithm.In view of the above problems,considering the community structure of the network and the characteristics of the connection nodes between the communities,the node’s own influence on the community and the strength of the connection with other nodes of the community are simultaneously integrated so as to obtain an effective and accurate seed collection so that the information can be rapidly transmitted to The internet.Finally,the two algorithms proposed in this paper use different evaluation criteria and compare the simulation results with other algorithms.Experiments show that the ranking results based on the triangular neighborhood node and k-shell importance rankingalgorithm are superior to other algorithms.The seed set selected by the hybrid impact maximization algorithm enables faster and broader dissemination of information than other algorithms.