Disease propagation control method based on network division

Abstract

The invention provides a disease propagation control method based on network division. The method comprises the following steps: firstly, extracting target network information, and initializing an empty sequence with edge importance from large to small; then, for the maximum connected piece in the network, network coarsening processing is completed through node merging, and then preliminary division of the network is completed through node exchange and network division; then, recovering the combined nodes into two nodes, completing network refinement, dividing the refined network, carrying out importance sorting on the deleted connecting edges, adding to the tail part of an edge importance sequence, replacing the initial network with the refined network, and carrying out repeated processing to obtain a new network; finally, the edges in the new network are subjected to importance sorting again and added to the tail of the edge importance sequence, and the average infection rate is used for representing and controlling the virus transmission degree. 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 method for controlling disease transmission based on network division. Background technique [0002] Some complex relationships in the real world can be described by networks, entities in the network can be represented by nodes in the abstract network, and links between entities can be described by edges. The application of the network is beneficial to the study of the global properties of the system, such as the stability of the power system, the mechanism of rumor spreading in the communication network and the virus spreading mechanism in the contact network. 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. Among them, the research on communication control in complex networks is of great significance fo...

AI Technical Summary

Problems solved by technology

However, its existing problems mainly include the following points: when the network scale is relatively large, the time complexity of the algorithm is too high, resulting in too long calculation time, and even the result cannot be calculated; only a single node is considered, and the overall network topology is ignored The importance of structure; the effectiveness of the algorithm is not enough, that is, after deleting a certain number of edges or nodes, the effect on propagation control is not great, but the network structure has been greatly damaged

These problems lead to the low applicability of these methods in reality, and solving these problems can help improve the efficiency of propagation control and computational efficiency

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