Complex network community discovery method based on spectral clustering improved intersection

A complex network and community discovery technology, applied in the field of community structure discovery in complex network research, can solve problems such as local optimum and slow convergence speed

Inactive Publication Date: 2016-02-03
BEIJING UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0005] In order to solve the problems of slow convergence speed and easy to fall into local optimum in complex network community mining methods, the present invention proposes a new method (Genetic Algorithmwithan improved Crossover based on Spectral Clustering for Community Mining, GACSCM) for complex network community mining based on spectral clustering improved crossover genetic algorithm

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  • Complex network community discovery method based on spectral clustering improved intersection
  • Complex network community discovery method based on spectral clustering improved intersection
  • Complex network community discovery method based on spectral clustering improved intersection

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Experimental program
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Embodiment Construction

[0052] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0053] figure 1 It is a flow chart of a method for mining complex network communities based on spectral clustering crossover genetic algorithm, the method includes the following steps:

[0054] Step 1, population initialization

[0055] Step 2, calculate the individual fitness function Q.

[0056] Step 3, spectral clustering division.

[0057] Step 4, cross operation, the specific flow chart is as follows figure 2 shown.

[0058] Step five, mutation

[0059] Step six, choose

[0060] Step 7, decode, and get the community division result of the complex network

[0061] The data that experiment of the present invention adopts is that Newman provides dolphin network (dolphin), Krebs American political book network (polbooks), jazz band cooperation network, and the information description of each network is as shown in table 1.

[0062] T...

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Abstract

The invention provides a complex network community discovery method based on spectral clustering improved intersection. Individuals in a population are divided by adopting spectral clustering having the advantages that clustering can be performed on any shapes of sample space, and genetic operation is performed by selecting the individuals of different divisions in intersection operation so that population diversity is increased and falling into local optimum can be avoided; and the similar individuals can effectively maintain the excellent characteristics of the individuals and maintain evolutionary direction of the population even the similar individuals cannot effectively increase population diversity so that genetic operation is also performed by the individuals of the same division when genetic operation of the individuals of different divisions is performed, and the two optimal individuals in the individuals generated by the two modes are selected to act as filial generation individuals. Intersection operation of the two modes is performed simultaneously so that falling of the algorithm into local optimum and low convergence speed can be avoided, convergence speed can be adjusted and balance between the optimal solutions can be searched.

Description

technical field [0001] The invention belongs to the technical field of complex network community discovery methods, specifically a new method of introducing spectral clustering to improve crossover genetic algorithms used in complex network community discovery. It is a method that uses computer technology, genetic algorithms, and spectral clustering A method for community structure discovery in complex network research implemented by methods such as . Background technique [0002] A large number of complex systems in nature and human society can be described by different networks. When using the network to describe the complex system, the nodes in the network are used to represent the individuals in the complex system, and the edges between the nodes represent the connections between the individuals in the system. Network systems in different fields in the real world, such as social networks, biological networks, information networks, technological networks, transportation ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06N3/12
Inventor 杨新武杨丽军
Owner BEIJING UNIV OF TECH
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