| The shortest path query problem of Top-k critical nodes in time-varying networks is related to scheduling and network optimization.The problem is to find the first k key nodes on the shortest path,where the node’s key is calculated according to the feasible path set.In recent years,a lot of research results have been produced in the aspect of network optimization model.However,with the rapid development of the network space,more and more time is spent on network security,and the means and types of network attacks are becoming more and more complex and refined,which brings greater challenges to network security.In communication,transportation network,network security risk assessment and a series of daily applications will involve a lot of new problems.This paper studies the relevant problems based on time-varying neural network framework model and algorithm,mainly studying the time-varying shortest path problem of critical node constraint,the Top-k critical node query problem of timevarying shortest path and the application of top-k critical unit query in security.The main innovation points are as follows:First,research on critical node neural networks(CNNN)with time-varying shortest paths constrained by critical nodes.The network studied in this problem is a kind of time-varying automatic wave neural network.The so-called automatic wave means that the network can compute and send automatic waves without training,and it can obtain the shortest path of the key node constraint under the time-varying network environment.In this part,automatic wave neural network(CNNN)is composed of six parts.In this paper,the NY data set is analyzed and compared with other algorithms.The results show that the CNNN algorithm in this paper is superior to other algorithms in terms of both time and relative error.Second,Top-k critical node auto-wave neural network for time-varying shortest paths.The network studied in this problem is a kind of time-varying auto-wave neural network,which can obtain the importance order of nodes on the shortest path.In this part,the time auto-wave neural network(TANN)consists of seven parts.Compared with other three algorithms on four standard network data sets,the results show that the TANN algorithm in this paper is superior to other algorithms in terms of both time and relative error.Third,on the research of automatic wave neural network for node security,this algorithm can solve the optimal solution of top-k key node security problem.The problem solved is to conduct network security risk assessment on the key nodes after finding the top-k key nodes in the network.Security assessment is to find the security performance of the key nodes in the network based on the topology structure of the network.In this part,the node security automatic wave neural network consists of six parts.In this paper,the NY data set is analyzed and compared with other algorithms,and the results show that the NSANN algorithm in this paper is superior to other algorithms both in terms of time and relative error. |
