Research On Social Network Node Influence Maximization Algorithm Based On K-shell

In recent years,with the development of various social platforms and the widespread popularity of online social networking,such platforms have become an indispensable part of most people's lives,and people use such channels for daily communication.Social networks are also constantly changing people's living habits,and people are more accustomed to obtaining news in this way.In the field of social network research,a very critical link is the judgment of the influence of nodes in the network.Therefore,it is necessary to analyze the magnitude of influence.This problem is the basis for maximizing the influence of nodes.Among them,the K-shell algorithm is a more effective influence division algorithm.The algorithm can quickly layer nodes in a network,and judge the influence of the nodes according to the level of the node.Therefore,the K-shell algorithm has been It has been a hot topic for people to study in the field of social networkingIn this paper,regarding the problem of inaccurate division of nodes in the network by the traditional K-shell algorithm,a K-shell-based algorithm for maximizing the influence of social network nodes is proposed.The parameters in the shell decomposition process,such as the number of iterations,node level,etc.,and comprehensively consider the influence of the location attribute of the node in the network and the neighborhood attribute on the influence of the node.The iterative rounds of the neighboring nodes in the K-shell decomposition process are combined with the distance from the current node to the network center to determine the position attributes of the nodes,and when calculating the effect of the nodes on the neighborhood,the degree and position of the neighboring nodes are calculated.Work together,and finally determine the final influence of a node through two aspects of location attribute and neighborhood attribute,and use the method proposed in this paper to verify the correctness of the improved algorithm ranking.In addition,in the process of selecting the seed node set,full consideration has been given to the overlap of the influence of nodes during propagation.The position of the node is used to reduce the influence of the node to varying degrees,which solves the influence in the process of influence propagation.The overlap phenomenon effectively eliminates the problem of wasting influence.Finally,an influence maximization algorithm is proposed by combining the above two schemes.Finally,a comparative simulation experiment is carried out on four real data sets.Experimental results show that the improved algorithm proposed in this paper is more accurate than the original algorithm and other related algorithms.Experiments show that the improved algorithm can improve the propagation effect in all kinds of data sets.