Research On Vital Node Identification Method Of Hypernetwork

In recent years,complex systems and complex networks have developed rapidly.As a new interdisciplinary subject,complex networks have attracted wide attention from different fields.However,the real network is a comprehensive network,which has the characteristics of big data,complexity,randomness and community.The traditional complex network can not simulate large scale,large number of nodes and edges Multi,multi-dimensional,multi-attribute,multi criteria of the real scene,therefore,the hypernetwork came into being.At present,the research of hypernetwork is still in the development stage,and the identification of vital nodes in hypernetwork has become a research direction gradually.The research of vital nodes in hypernetwork has important theoretical and practical value for a clearer understanding of hypernetwork and the study of topology and functional characteristics of hypernetwork.In this paper,the vital node identification method and application of hypernetwork are studied:1.The K-shell index based on the idea of complex network location is extended to the hypernetwork to avoid the impact of nodes with large degree of hypernetwork but located at the edge of the hypernetwork on the mining of vital nodes.Due to the limitation of K-shell method,the sorting result of nodes is too rough.In order to solve this problem,this paper proposes a CKsd(complex K-shell and degree)index to identify the vital nodes of the hypernetwork by combining the hyperdegree and K-shell(Ks)values and using the Euclidean distance formula,and verifies it by using the protein complex data set in the online gene necessity database.Experiments show that CKsd index can accurately and effectively identify the vital nodes in the hypernetwork.2.Based on the theory and properties of hypergraph,this paper proposes the adjacency structure entropy of hypergraph to identify the vital nodes in the hypernetwork.By studying the relationship between the nodes and their direct and indirect nodes,this method uses the node information entropy to describe the importance of different nodes in the hypernetwork.The advantage of this algorithm is that we not only consider the properties of the nodes in the hypernetwork,but also integrate the influence of neighbor nodes.Moreover,because this algorithm only uses the local attributes of nodes,its complexity is low.And through the empirical analysis of the research cooperation hypernetwork,the results show that the adjacency structure entropy can accurately and effectively identify the vital nodes in the hypernetwork.The purpose of this paper is to study the identification index of vital nodes in hypernetwork,and propose k-shell based and adjacency structure entropy based vital node identification algorithms for hypernetwork.The experimental results show that the two algorithms can better identify the vital nodes in the hypernetwork,and provide some help for the analysis and research of the hypernetwork structure in the future.