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Multi-target community detection method based on k node updating and a similarity matrix

A technology of similarity matrix and detection method, which is applied in the direction of gene model, genetic rule, instrument, etc., can solve the problems of low detection accuracy of time-consuming community, inability to deal with symbolic social network, and low detection accuracy, so as to improve the detection accuracy of community , reduce useless searches, and improve detection accuracy

Inactive Publication Date: 2017-07-07
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing multi-target community detection technology has the following disadvantages: (1) It does not make full use of the prior information of the network, and the design flaw of the genetic operation method causes the existing technology to carry out a lot of useless searches in the search space, resulting in a lot of time overhead And the accuracy of community detection is not high; (2) most community detection methods can only deal with unsigned social networks (abbreviated as unsigned networks), but cannot deal with symbolic social networks (referred to as symbolic networks); (3) a small number of community detection methods Technique designed for signed networks, but cannot handle or detect poorly on unsigned networks
[0005] In short, the relatively single type of network to be processed and the low detection accuracy are the main defects of the existing community detection technology.

Method used

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  • Multi-target community detection method based on k node updating and a similarity matrix
  • Multi-target community detection method based on k node updating and a similarity matrix
  • Multi-target community detection method based on k node updating and a similarity matrix

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Experimental program
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Effect test

Embodiment 1

[0036]Complex networks are an abstract description of complex systems in the real world, such as social networks, biological protein systems, and power systems can all be abstracted into complex networks. Community, also known as module, is a very common and extremely important topological attribute in complex networks. It refers to a collection of nodes whose internal connections are closer than external connections in the network. The detection of community structures in complex networks is a method or technology for mining community structures in complex networks, which is of great significance for workers in related fields to understand the structural organization forms and organizational functions in real systems. For the existing community detection technology, the commonly used technology is to establish an optimization model for a specific type of network, and solve the model by designing an optimization method, and finally obtain the network division mode, that is, the...

Embodiment 2

[0063] The multi-target community detection method based on k-node update and similarity matrix is ​​the same as in embodiment 1, and the specific calculation formula of the similarity matrix S of the input network node described in step 3 is as follows:

[0064]

[0065] Among them, S(v i ,v j ) means node v i with node v j The similarity value between represents the element of row i and column j in the similarity matrix S; Γ + (v i ) means that with node v i The set of adjacent nodes with a positive relationship, Γ - (v i ) means that with node v i The set of adjacent nodes with negative relationships; |Γ + (v i )∩Γ + (v j )| represents node v i with node v j The number of positive neighbors shared by |Γ - (v i )∩Γ- (v j )| represents node v i with node v j The number of negative neighbors shared by each other; represents node v i degree, that is, with node v i The sum of the number of connected edges.

[0066] The invention expands the similarity f...

Embodiment 3

[0068] The multi-target community detection method based on k-node update and similarity matrix is ​​the same as embodiment 1-2, and the k-node update strategy described in step 4 includes:

[0069] Arrange the elements in each row in the similarity matrix S in descending order, and take the first k adjacent nodes whose similarity is greater than 0, denoted as V k_neighbor , and use the community labels of these adjacent nodes to update the community labels of each individual in the population pop according to the following rules. run r t The preprocessing process of the network can be completed in one time, and the sub-community structure of the network can be obtained.

[0070]

[0071] Among them, Γ(v i ) means node v i The meaning of the formula in the curly brackets represents counting the community categories, and r is the community label value of the node. This means counting the community label values ​​of the k nearest neighbor nodes, and obtaining the communit...

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Abstract

The invention discloses a multi-target community detection method based on k node updating and a similarity matrix, so that problem that the community detection effect is poor and the multi-type network can not be process in the prior art can be solved. The method comprises: network data are inputted; a population and a weight vector are initialized to obtain each weight vector neighbor subscript set; a similarity value between any two nodes in a network is calculated to obtain a similarity matrix; network pre division is carried out by using a k node updating strategy; an individual objective function value in the population is calculated and the objective function reference point is initialized; evolution of individuals in the population is carried out to obtain offspring groups; an offspring group objective function value is calculated and the objective function reference point is updated by the objective function value; neighbors of individuals in a current-generation population are updated by using the offspring groups; a maximum evolutional generation is reached and detection is ended; and otherwise, transferring to population evolution is carried out until community detection is completed.

Description

technical field [0001] The invention belongs to the technical field of community mining in complex networks, and mainly relates to multi-target community detection, in particular to a multi-target community detection method based on k-node update and similarity matrix, which can be used for community detection in unsigned and signed social networks. Background technique [0002] Complex networks are often used to describe various complex systems in real networks. In recent years, with the rapid development of the Internet, the research on community detection technology in social networks has become an important branch in the field of complex network research. In the field of complex networks, a community refers to a collection of nodes whose internal connections are relatively tight compared to external connections, also known as modules. For community detection in social networks, it is possible to understand the structural organization and functional modules in the networ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06N3/12
CPCG06Q50/01G06N3/126
Inventor 尚荣华刘欢焦李成刘芳马文萍王蓉芳马晶晶王爽侯彪
Owner XIDIAN UNIV
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