Large-scale complex network community discovery method based on local neighbor information and application

Abstract

The invention discloses a large-scale complex network community discovery method based on local neighbor information, and the method comprises the following steps of A, obtaining a network structure,and selecting an unvisited node with the maximum selection degree and all neighbor nodes of the unvisited node as an initial community; B, deleting the nodes which are not tightly connected in the community; C, selecting an alternative node to expand the initial community by utilizing the local information of a network; D, calculating a node membership coefficient to judge whether the node shouldbe left, obtaining a community, and marking all nodes of the community as accessed nodes; and returning to the step A until no unvisited node exists, and outputting a community division result. The invention further provides a computer virus propagation control method which comprises the steps of constructing a computer network model, using the above method for obtaining and dividing the communities, and mainly protecting the community core nodes and the boundary nodes. The method has the advantages that the method has the good calculation efficiency and algorithm precision on the super-large-scale problem.

Description

technical field [0001] The invention relates to the technical field of complex network community division, in particular to a large-scale complex network community discovery method and application based on local neighbor information. Background technique [0002] The complex network is modeled as a graph containing nodes and edges according to graph theory, which provides an effective way to represent the relationship between real-world system objects. Examples include social networks, computer networks, and biological networks. The main problem and difficulty in complex network research is the mining of community structure, that is, to divide a real network into a collection of nodes with dense internal connections and sparse inter-cluster connections. By mining the community structure of complex networks, it is helpful to better understand and mine the functional modules and topological structures of network systems. [0003] For example, establishing a network based on ...

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Problems solved by technology

[0006] Although the above method is fast, it still cannot effectively solve the ultra-large-scale complex network problems of the order of millions, and there are still the following problems:

[0007] (1) During the expansion process of the traditional community discovery method based on expansion, it is necessary to repeatedly calculate the benefit function value of the neighbor nodes of the current community, and the operation speed on the ultra-large-scale complex network is not ideal;

[0008] (2) Although some algorithms are fast and can quickly obtain the results of large-scale network community division, their accuracy is average, and the quality of the obtained overlapping communities needs to be improved

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