Virus propagation control method based on bounded seepage-greedy algorithm

Abstract

The invention provides a virus propagation control method based on a bounded seepage-greedy algorithm. Firstly, target network information is extracted, and target network nodes and connection edge attributes are known; then, on the basis of the percolation theory, specific nodes are continuously occupied on the target network, and the reverse process of removing key nodes is achieved; occupying a node which minimizes an objective function every time, limiting the size of a maximum connected component and the number of residual network nodes, and controlling virus propagation; setting a key index boundary value, and when the key indexes of all candidate nodes exceed the boundary value, updating the boundary value to occupy more candidate nodes; and finally, expressing the effect of controlling virus propagation by using sequence parameters and network toughness. According to the method, rapid and efficient decomposition of a large network can be realized, so that virus propagation is controlled in time, and loss caused by virus propagation is reduced.

Description

technical field [0001] The invention belongs to the technical field of network information analysis, and in particular relates to a virus propagation control method based on a bounded seepage-greedy algorithm. Background technique [0002] The network can simulate the interaction within a complex system, where nodes represent individuals in the system, and edges represent the interaction between individuals. The application of network is beneficial to study the global properties of the system. The network can test the effects of various artificial measures used in real systems, and provide optimal solutions for control, prediction, optimization, and reconstruction of real systems. [0003] The network decomposition problem refers to identifying a set of critical nodes for a given network, the removal of which can maximize the decomposition of the network. Network decomposition can effectively reflect and analyze the real situation. For example, compared with the diffusion ...

AI Technical Summary

Problems solved by technology

In practice, in the face of a large-scale outbreak of virus transmission, it is necessary to take into account real-time information from all parties, and quickly respond to prevent and control; however, the above methods are difficult to apply to large-scale networks in practice, and it is difficult to achieve rapid response to virus transmission

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