Complex network community detecting method based on prior information and network inherent information

A complex network and inherent information technology, applied in the direction of genetic models, etc., can solve problems such as too many invalid searches, poor network division results, and unstable operation results of traditional genetic algorithms, so as to improve stability and accuracy and avoid loss Effect

Active Publication Date: 2015-01-07
XIDIAN UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to overcome the poor network division results caused by the inability of the traditional genetic algorithm to make good use of the inherent information contained in the original

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  • Complex network community detecting method based on prior information and network inherent information
  • Complex network community detecting method based on prior information and network inherent information
  • Complex network community detecting method based on prior information and network inherent information

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Embodiment Construction

[0030] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0031] Step 1: Construct an adjacency matrix A corresponding to the complex network to be detected.

[0032] Node and connection information in a complex network is usually expressed in the form of a graph, which is represented as a collection of nodes and edges. When dividing complex networks into communities, the specific network can be abstracted into a graph G(V,E) composed of point sets and edge sets, V represents a set of nodes, E represents a set of edges, and each edge in E is There is a pair of points of V type corresponding to it. Usually an adjacency matrix is ​​used to represent and store graph G(V,E). For graph G(V,E), construct adjacency matrix A=A ij , if there is an edge connecting node i and node j in the graph, then the corresponding element A in the adjacency matrix A ij = 1, otherwise A ij =0, so there are only two element values ​​of 0 and 1 in the a...

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Abstract

The invention belongs to the technical field of evolutionary computation and complex network community mining and discloses a complex network community detecting method based on prior information and network inherent information. The method is mainly used for community division of complex networks. The method comprises the steps of establishing a network adjacent matrix, initializing the population by means of adjacent matrix information, conducing preprocessing according to the inherent information of the adjacent matrix to reduce invalid searching, optimizing a modularity function Q, conducting gene interlace operation and mutation operation, using the local search with mutation operator method (LSMM) based on variation and network inherent information, and testing a community division result by means of an evaluation function NMI. According to the method, the community network is detected by fully utilizing prior knowledge and inherent information contained in the network adjacent matrix, an optimal solution is obtained more effectively by means of the LSMM based on variation and network inherent information, and the community structures of a real world network and a synthetic network can be better found compared with an ordinary genetic algorithm.

Description

technical field [0001] The invention belongs to the field of evolutionary calculation and the technical field of complex network community mining, and relates to a method for community division of a network using an evolutionary calculation method in the field of complex network, in particular to a complex network community detection method based on prior information and inherent network information. Background technique [0002] There are various complex networks in the real world, such as dense transportation networks and power networks in cities; ecological networks in nature; and online relationship networks between people in human society. With the increasing development of society and technology, human beings need to have a better understanding of various natural and artificial complex networks. Therefore, complex network research has become one of the most important interdisciplinary research fields. Community structure is a common property of many complex networks i...

Claims

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Application Information

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IPC IPC(8): G06N3/12
Inventor 刘若辰焦李成李冰杰刘红英王爽马晶晶张向荣尚荣华
Owner XIDIAN UNIV
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