Research On Algorithm Of Node Importance Evaluation In Complex Network

Many networks in the real world can be abstracted as complex networks,such as communication networks,rail transit networks,and power transmission networks.Nodes in the network are interrelated,and once some critical nodes are subjected to external or internal attacks,it may lead to large-scale failures of the remaining nodes in the network,or even paralysis of the entire network.However,if preventive measures can be taken in advance to identify these key nodes in the network and their problems,it can greatly improve the network’s survivability and avoid the occurrence of the above problems.Therefore,identifying influential nodes in complex networks is particularly important.In this study,a hybrid topology algorithm is proposed to solve the vulnerability problem of complex network systems;Considering cascading failure reaction,this paper further proposes a dynamic evaluation algorithm.This topic mainly aims to evaluate the importance of complex network nodes from both static and dynamic aspects.Firstly,this study proposes a static node importance evaluation algorithm based on complex network theory-Hybrid Topology Structure(HTS)algorithm.This algorithm considers the local topology of nodes and their adjacent nodes,as well as the global topology,which can better filter out key nodes in complex networks.After the completion of the model construction,in order to verify the effectiveness of the algorithm,this paper evaluated the performance of the algorithm in all aspects in six real networks using susceptible-infected-recovered model,monotonicity,different metrics method,Jaccard similarity coefficient,and Kendall correlation coefficient as evaluation indicators.Experimental results confirm that the HTS algorithm has higher performance in identifying networks compared to previous research algorithms.In dynamic node importance assessment,the failure of one or more nodes can affect adjacent nodes and even cause most nodes in the network to fail.Therefore,considering the dynamic nature of cascading failure reaction is also of great significance for improving the security of complex networks.Based on this,this study proposes a dynamic node importance evaluation algorithm based on complex network theory-Dynamic Evaluation(DE)algorithm.This algorithm not only considers the impact of single node failure on the total network load,but also further explores the impact of single node removal on the efficiency of the entire complex network.In order to verify the effectiveness of the DE algorithm,this paper evaluates it on six real networks using the failure node ratio and the largest connected subgraph.The experimental results also verify the effectiveness of the DE algorithm.Here,this study also optimized existing recovery models.In the recovery model,the important nodes identified by the DE algorithm are repaired after they fail.Based on the experimental results,this paper obtains the optimal recovery model parameters for each real network.