Active learning multi-label social network data analysis method based on graph data

A social network, active learning technology, applied in the field of active learning multi-label social network data analysis based on graph data, can solve the problem of low accuracy of social network data analysis

Inactive Publication Date: 2015-12-23
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to provide an active learning multi-label social network data analysis

Method used

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  • Active learning multi-label social network data analysis method based on graph data
  • Active learning multi-label social network data analysis method based on graph data
  • Active learning multi-label social network data analysis method based on graph data

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Embodiment

[0037] figure 1 It is a flowchart of an active learning multi-label social network data analysis method based on graph data according to an embodiment of the present invention. Such as figure 1 As shown, the scene image labeling method involved in the present invention includes the following processes.

[0038] An active learning multi-label social network data analysis method based on graph data, which specifically includes the following parts:

[0039] Obtain social network user data from the server, construct the obtained data in the form of graph data, local and global consensus algorithm (LLGC), multi-label active learning evaluation model of data information value, direct push Rademacher complex Degree, minimization of generalization error bounds, prediction and recommendation for users.

[0040] The first step is to collect the occupation, uploaded images, hobbies, gender, location, graduate school, browsing history, shopping habits and other information of each soci...

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Abstract

The invention discloses an active learning multi-label social network data analysis method based on graph data, concretely comprising the steps of social network data acquisition, type marking and training, model building and social network user data analysis. The invention provides a multi-label graph data classification method, and is combined with an active learning method based on error bound minimality. A series of objective equations are obtained through multi-label classification and LLGC, and are applied to transductive Rademacher complexity. The method aims to minimize the experience transductive Rademacher complexity and to obtain a minimized generalization error bound, and thereby obtains a few nodes containing vast information on graphs. The method can classify massive multi-label graph data so as to provide support for subsequent decisions.

Description

technical field [0001] The invention belongs to the technical field, and in particular relates to an active learning multi-label social network data analysis method based on graph data. Background technique [0002] The "Internet +" strategy is to use the Internet platform and information communication technology to combine the Internet with all walks of life, including traditional industries, to create a new ecology in new fields. "Internet +" is a further practical result of Internet thinking. It represents an advanced productive force and promotes the continuous evolution of economic forms. Thus driving the vitality of social and economic entities and providing a broad network platform for reform, development and innovation. It represents a new social form, which is to give full play to the optimization and integration of the Internet in the allocation of social resources, deeply integrate the innovative achievements of the Internet into various fields of economy and soc...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/30
CPCG06F16/951G06F18/24133
Inventor 刘波李程文肖燕珊郝志峰余刚李远航
Owner GUANGDONG UNIV OF TECH
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