Research Of Node Importance Evaluation Algorithm Based On Path Information And Multi-attribute Decision

Accurately identifying the key nodes in the network has important theoretical guidance and practical application value in terms of network attack,disease immunity and information dissemination.For the existing algorithms,the node importance analysis is not comprehensive enough and cannot distinguish different nodes significantly.This thesis proposes two node importance evaluation algorithms from the local and global perspectives,and compares the proposed algorithm with the existing algorithms through relevant experiments to evaluate the evaluation effect of the proposed algorithm.The main work and results are as follows:1.In view of the fact that most existing algorithms ignore the influence of structural information between nodes on node importance when measuring node importance,a node importance evaluation algorithm based on gravity model and relative path number is proposed.Firstly,the influence of the relative shortest path number on the information dissemination between nodes is analyzed.At the same time,considering the influence of factors such as non-shortest paths and path lengths,the importance of nodes is defined by the sum of the forces of neighbor nodes on the central node in the third-order range.Finally,the simulation experiment is carried out to compare with the existing algorithm.The experimental results show that the proposed algorithm can not only clearly distinguish the importance difference between different nodes,but also accurately identify the key nodes that maintain network robustness and strong propagation ability.At the same time,the time complexity of the algorithm is low,and it is suitable for large networks.2.Aiming at the problems that most of the existing algorithms have insufficient node importance analysis,unreasonable index weight distribution,and imprecise comprehensive evaluation methods,a node importance evaluation algorithm based on improved CRITIC and grey TOPSIS is proposed.Firstly,the structural weakening factor is defined by the structural information between neighboring nodes,and new local centrality and global centrality are proposed by borrowing the idea of semi-local centrality.Secondly,the improved CRITIC method is used to objectively weight the two centrality indexes,and then combined with the grey TOPSIS method to comprehensively evaluate the importance of all nodes.Finally,simulation experiments are carried out in 9 real networks.The experimental results show that the proposed algorithm can significantly distinguish the importance difference between different nodes,and can also accurately evaluate the importance of network nodes.At the same time,the experimental results of the proposed new local centrality and global centrality are also better than most algorithms,which reflects the rationality and effectiveness of the index selection.