Study On Node Importance Ranking In Multilayer Complex Network

Complex network analysis is a research hotspot in the current academic world,node importance ranking in complex networks is one of its important research contents.The existing node importance ranking algorithms are mostly analyzed from the perspective of single attributes.The evaluation is not comprehensive enough,which affects the accuracy of the ranking results.At the same time,there are many kinds of interactions between entities in the real world.It is necessary to use multilayer complex network to represent multiple interactions between entities.However,the existing method of node importance ranking is mostly for single layer complex networks,cannot be directly applied to multilayer complex network.This paper presents a node importance ranking algorithm based on degree and Kshell iteration number.Firstly,the fusion of the degree of the node and K-shell iteration number are used as the weighting factors of the node,and node importance is evaluated from the local and global perspectives.Secondly,the algorithm combines the weighting factors of the node itself and its neighbor nodes to determine the importance of the node.Considering that the contribution of different factors to node importance is not exactly the same,this paper uses the entropy weight method to determine the influence weight of the degree and the K-shell iteration number on the importance of the node.Experiment results of real network and artificial network show that the proposed algorithm in this paper has higher accuracy than traditional node importance ranking algorithm and has higher execution efficiency.This paper presents a node importance ranking algorithm in multilayer complex network based on grey relational analysis.Firstly,the node importance ranking algorithm of based on degree and K-shell iteration number is used to obtain the importance of nodes in single layer network.Secondly,the sample matrix is constructed based on the importance of the nodes in all single layer networks,and the matrix is normalized.Finally,the grey relational analysis method is used to calculate the grey correlation degree between nodes and ideal objects,which is the importance of nodes in multilayer complex network.Experiment results of three real multilayer networks show that the proposed algorithm in this paper has high accuracy,and it can provide more reasonable ranking results than the ranking results in single relational networks and clustering network.