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Adaptive spectral clustering method of extracting network node community attribute

A technology of community attributes and network nodes, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of inaccurate node clustering, and achieve the effect of improving accuracy and numerical accuracy

Active Publication Date: 2016-06-22
保定笙墨信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The main purpose of the present invention is to disclose an adaptive spectral clustering method for extracting network node community attributes, overcome the problem of inaccurate node clustering in the prior art, obtain better node community attributes, and improve the accuracy of network node community classification Spend

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  • Adaptive spectral clustering method of extracting network node community attribute
  • Adaptive spectral clustering method of extracting network node community attribute
  • Adaptive spectral clustering method of extracting network node community attribute

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Embodiment 1

[0022] refer to figure 1 , the adaptive spectral clustering method of an embodiment of the present invention includes the following steps:

[0023] Step 1. Set the number of nodes in the mobile crowd-sensing network as M, and regard M nodes as a community, that is, initialize the number of communities N=1, and mark the community attribute of node v as , , , defining the modularity Q of a community composed of M nodes max =0;

[0024] Step 2. From the intimacy vector of node v to all M nodes Get the similarity matrix:

[0025] ,

[0026] in, is the intimacy between node v and node w, when there is no contact between nodes, =0;

[0027] Step 3, the similarity matrix The eigenvalues ​​are arranged from large to small, and the first N eigenvalues ​​are taken to construct a eigenvector space, and the K-means method is used to cluster the eigenvector space, and mark the community attribute of each node;

[0028] Step 4. According to the community attributes of al...

Embodiment 2

[0037] The adaptive spectral clustering method of another embodiment of the present invention includes the following steps:

[0038] Step 1. Set the number of nodes in the mobile crowd-sensing network as M, and regard M nodes as a community, that is, initialize the number of communities N=1, and mark the community attribute of node v as , , , defining the modularity Q of a community composed of M nodes max =0;

[0039] Step 2. From the intimacy vector of node v to all M nodes Get the similarity matrix:

[0040] ,

[0041] in, is the intimacy between node v and node w, when there is no contact between nodes, =0;

[0042] Step 3, the similarity matrix The eigenvalues ​​are arranged from large to small, and the first N eigenvalues ​​are taken to construct a eigenvector space, and the K-means method is used to cluster the eigenvector space, and mark the community attribute of each node;

[0043] Step 4. According to the community attributes of all nodes after cl...

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Abstract

An adaptive spectral clustering method of extracting a network node community attribute is disclosed. The method comprises the following steps of 1, setting the number of nodes in a mobile group intelligence perception network to be M and defining modularity of one community formed by the M nodes to be Qmax, wherein an initialized community number N equals to 1, a community attribute of a marked node v is Cv and the Qmax equals to 0; 2, acquiring a similarity matrix through an intimacy vector of the node v relative to the whole M nodes; 3, arranging characteristic values of the similarity matrix from large to small, clustering characteristic vector spaces constructed by the first N characteristic values and marking a community attribute of each node; 4, using the community attributes of all the clustered nodes to calculate the modularity Q, if the Q is greater than or equal to Qmax, making the Qmax equal to the Q and making an optimum community classification number Nop equal to the N, otherwise, directing entering into step 5; 5, making N equal to N+1; 6, repeating step3 to step5 till that the N equals to the M, the Nop value is the optimum community classification number and the nodes in the community possess the optimum community attribute. By using the method, accuracy of the network node community classification can be increased.

Description

technical field [0001] The invention relates to a mobile crowd sensing network (MCSN), in particular to an adaptive spectrum clustering method for extracting community attributes of network nodes. Background technique [0002] In the mobile crowd sensing network MCSN, the opportunistic data transmission mode based on store-carry-forward wireless multi-hop is often used to collect sensing data. In the weakly connected state of MCSN, the key to opportunistic data transmission is to find a better relay node selection strategy. In the prior art, the relay node selection strategy is: all nodes in the mobile crowd-sensing network are divided into multiple communities, and in the process of message forwarding, the message is preferentially forwarded to the node in the community where the destination node is located. By comparing the global centrality of nodes, the message is forwarded to the node with high global centrality, and in the community where the destination node is locat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 马华红郑国强吴红海冀保峰祁志娟徐素莉李济顺李阳袁德颖周立鹏
Owner 保定笙墨信息科技有限公司
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