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Social network friend recommendation method based on community division

A community division and social network technology, applied in neural learning methods, biological neural network models, genetic models, etc., can solve problems such as incomplete data

Inactive Publication Date: 2018-11-02
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

(2) Links that already exist at this moment, but are not recognized due to incomplete data and other issues

Method used

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  • Social network friend recommendation method based on community division
  • Social network friend recommendation method based on community division
  • Social network friend recommendation method based on community division

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Experimental program
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Embodiment Construction

[0065] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0066] figure 1 To do community division based on improved genetic algorithm based on spectral clustering, the method includes the following steps:

[0067] Step 1: Initialize the population P;

[0068] Step 2: Calculate the fitness function of the individual in P;

[0069] Step 3: The spectral clustering algorithm performs population division on P;

[0070] Step 4: Perform crossover and mutation operations to generate a new population C;

[0071] Step 5: The selection strategy selects the optimal population individual from P and C and puts it into P;

[0072] Step 6: Judging whether the number of iterations has been reached, if yes, go to step 7, otherwise go to step 3;

[0073] Step 7: Select the best individual from P, and after decoding, it is the optimal division of the community;

[0074] figure 2 For the link prediction algorithm...

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Abstract

The invention discloses a social network friend recommendation method based on community division. Link prediction is to predict the connection possibility between two points according to an existingnetwork topological structure, node property information and the like. Most existing node similarity algorithms only consider information of common neighbor nodes, that is, a topological structure with path length of 2, the important information that some nodes possibly belong to the same community is ignored, and obviously the nodes in the same community are more possible to have links. Accordingto the defects of traditional link prediction methods, an improved genetic algorithm is mainly used to perform community division on all the nodes first, then link prediction is performed according to the community division result, and therefore a social network friend recommendation algorithm based on community division is proposed. By doing contrast tests among five real networks, the accuracyof the algorithm compared with the traditional node similarity algorithms is analyzed through comparison, and the availability of the algorithm is proved.

Description

technical field [0001] The invention belongs to the field of complex network link prediction. Specifically, it uses an improved genetic algorithm to divide all nodes into communities, and then performs similarity prediction algorithms. It is a new method used in social network friend recommendation. Background technique [0002] A complex network refers to a network with a large number of nodes and a complex connection structure between nodes. Complex networks have properties such as self-organization, self-similarity, attractors, small-world, and scale-free. In real life, systems that can be described as complex networks are very common. Such as transportation network, financial relationship network, paper citations, social network, etc. By studying complex networks, we can more clearly analyze the network structure and its evolution process in real life. [0003] The problem of link prediction is one of the research directions of complex networks. It uses known networ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06N3/12
CPCG06N3/08G06N3/126G06N3/047G06F18/23
Inventor 杨新武张煜尚雨薇
Owner BEIJING UNIV OF TECH
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