Virus propagation control method based on bounded seepage-greedy algorithm

A greedy algorithm and control method technology, applied in the field of network information analysis, can solve problems such as difficult to achieve rapid response to virus spread, and achieve the effects of small virus spread, few removed nodes, and good protection

Pending Publication Date: 2022-03-25
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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  • Claims
  • Application Information

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

Method used

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  • Virus propagation control method based on bounded seepage-greedy algorithm
  • Virus propagation control method based on bounded seepage-greedy algorithm
  • Virus propagation control method based on bounded seepage-greedy algorithm

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0038] Such as figure 1 As shown, the present invention provides a kind of virus transmission control method based on bounded percolation-greedy algorithm, and its specific implementation process is as follows:

[0039] Step 1: Input the data related to the population involved in virus transmission, including individual information, number of individuals, connection between individuals and the probability of spreading the virus. The individual in the data of the virus transmission population is used as a node, and the connection between individuals is an edge. The probability of spreading the virus among them is the weight of the edge, and the virus spreading network G(N, M) corresponding to the data of the virus spreading population is constructed. The point set of the net...

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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 ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80
CPCG16H50/80
Inventor 刘洋陈晓祺王震王茜李学龙
Owner NORTHWESTERN POLYTECHNICAL UNIV
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