Method for genetic algorithm with local modularity for community detecting

A genetic algorithm and complex network technology, applied in the field of complex network community mining, can solve the problems of slow convergence speed and high time complexity, and achieve the effect of pertinence, low time complexity and increased migration

Active Publication Date: 2015-07-22
JIANGSU BOZHI SOFTWARE TECH CO LTD
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Problems solved by technology

[0005] In order to solve the problems of high time complexity and slow convergence speed in complex network community mining methods, the present invention provides a genetic algorithm based on local modularity for large-scale complex network community mining (Genetic Algorithm with Local Modularity for Community Detecting, referred to as LMGACD) new method

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  • Method for genetic algorithm with local modularity for community detecting
  • Method for genetic algorithm with local modularity for community detecting
  • Method for genetic algorithm with local modularity for community detecting

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Embodiment Construction

[0050] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0051] figure 1 It is a flow chart of a method for mining a large-scale complex network community based on a genetic algorithm based on local modularity. The method includes the following steps:

[0052] Step 1, coding the network community division.

[0053] Step 2, population initialization.

[0054] Step 3, calculate the fitness function.

[0055] Step 4: Carry out genetic operations: crossover, mutation, selection, the flow chart of the mutation operation is as follows figure 2 shown.

[0056] Step five, decoding to obtain the best community division.

[0057] An example of applying the present invention is given below.

[0058] The data used in the experiment of the present invention is the Zachary Karate Club Network (Karate Club Network) network provided by Newman, the American College Football League network, the Dolphin Network...

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Abstract

The invention relates to a method for genetic algorithm with local modularity for community detecting and belongs to the technical field of complex network community mining. The method comprises the steps of encoding network community division; initializing populations; calculating fitness functions; performing genetic operation: crossing, mutation and selection; and performing decoding to obtain optimum community division. According to the genetic algorithm method, roulette selection is added in a crossing operator rather than individuals in the populations are selected randomly for crossing operation, so that the high-fitness individuals have priority selective properties, and generation of optimum division can be accelerated; a local modularity function is introduced in a mutation operator, so that a mutated candidate solutions is close to an optimal solution, the local search capacity of the mutation operator can be improved, the pertinency is achieved, and the search performance of the algorithm is improved; and a good division effect can be obtained when a genetic algorithm with local modularity for community detecting (LMGACD) is used for mining complex network communities, and the time complexity is low.

Description

technical field [0001] The invention belongs to the technical field of complex network community mining, and specifically relates to a method for mining large-scale complex network communities based on a genetic algorithm based on local modularity, which is a method for realizing complex network community mining by using computer technology, genetic algorithms, and the like. Background technique [0002] Complex networks are typical manifestations of complex systems, and community structure is one of the most important structural features of complex networks. Detecting meaningful communities in complex networks plays an important role in network modeling and analysis. Community structure is a structural characteristic between macro and micro in complex networks, and it is a similar organization method of network nodes. The key feature of community structure is that the connection density between nodes within a community is higher than that between communities. Detecting th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/12G06Q50/00
Inventor 杨新武李瑞
Owner JIANGSU BOZHI SOFTWARE TECH CO LTD
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