Research On The Importance Of Node In Social Network

With the rapid development of Internet technology,the structure of social network is becoming increasingly complex,and the research on social network has always been a hot topic.How to quickly and accurately evaluate the importance of node in social network is of great significance to the follow-up research on mining core nodes with high influence,community discovery and influence maximization of social network.Research on the importance of node in social network has achieved some mature research results,which have been applied in the fields of biology,physics and social life.This paper mainly studies evaluation algorithms of node importance in social network.The specific work and results are as follows.In this paper,a node importance evaluation algorithm based on K index of neighbor nodes is proposed,and we extend the K index used for the author's influence evaluation to the social network node importance evaluation field.Firstly,the K index value of each node is calculated as the importance of the node itself,and then the importance of the neighbor nodes is calculated according to the indirect influence component of the neighbor nodes proposed in this paper.Finally,the direct influence of the node itself and the indirect influence of the neighbor nodes on it are considered as the importance of the node.Compared with the existing algorithms in three aspects: discrimination,accuracy and time efficiency,the experimental results show that the proposed method has high discrimination,accuracy and low time consumption.The method proposed in this paper shows better performance in networks with different topology,and it can be seen our method has good stability.In this paper,an algorithm for evaluating the importance of temporal network node based on the location of node is proposed.Based on the existing temporal network model,the K-shell decomposition algorithm is extended to the temporal network,and the location information of network node is obtained by classical K-shell decomposition algorithm.The temporal information of the temporal network is preserved by calculating the weight of different temporal slices,and the node importance in the temporal network is calculated by combining with the node position information obtained by the K-shell decomposition algorithm,an algorithm for evaluating the importance of the temporal network node based on the location of the node is proposed.The validity of the method proposed in temporal network model is demonstrated by experiments of discrimination and accuracy in real temporal network datasets.In this paper,by adjusting the classical SIR model and extending it to the temporal network based on the temporal network model.A method for measuring the performance of the importance evaluation algorithm on temporal network is proposed,which is used to verify the performance of the location-based node importance evaluation algorithm on temporal network proposed in this paper.