Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Semi-supervised learning-based method for filtering junk users in social network

A semi-supervised learning and social network technology, applied in network data retrieval, network data indexing, data processing applications, etc., can solve labeling bottlenecks, time-consuming and labor-intensive problems

Active Publication Date: 2017-01-04
CHONGQING UNIV OF POSTS & TELECOMM
View PDF3 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Most of the traditional spammer detection methods are classification models based on supervised learning. In order to improve the generalization ability of the classifier, a certain number of labeled samples must be added. However, the acquisition of such samples requires manual labeling. It is time-consuming and labor-intensive, and it is easy to form a labeling bottleneck problem

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Semi-supervised learning-based method for filtering junk users in social network
  • Semi-supervised learning-based method for filtering junk users in social network
  • Semi-supervised learning-based method for filtering junk users in social network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035]Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar meanings throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0036] figure 1 It is a schematic diagram of the overall process structure of the present invention. As shown in the figure, the present invention provides a method for filtering social network junk users based on semi-supervised learning. First, information gain feature selection is performed on the high-dimensional social network data; then the training sample set is used to train and learn by using the Tri-training algorithm to obtain the optimal classifier; finally, the performance of the classifier is evaluated by using the test sample set. The specific steps are as ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a semi-supervised learning-based method for filtering junk users in a social network. A cooperative training algorithm is applied to the detection of the junk users in the social network. Massive information in the social network is classified mainly by utilizing a supervised learning algorithm at present; and the algorithm is based on a classification model built based on annotated data, but the social information scale is huge, the labor cost required for data annotation is high, and few methods for solving the problem of user data annotation of the social network exist. A method is proposed; and by referring to the cooperative training algorithm, multiple views and multiple classifiers are applied to a large amount of non-annotated social network data or a small amount of annotated social network data, so that the classifiers on different views learn mutually and the purpose of data annotation is achieved.

Description

technical field [0001] The invention relates to the field of social network security, in particular to a semi-supervised learning-based method for filtering social network junk users. Background technique [0002] The vigorous development of social networks (Social Networks, SN) has become a global social phenomenon. At present, the number of social networks is increasing rapidly, and the scale of users is constantly expanding. Among these Internet user groups, social networking has become an irreplaceable means of their daily communication. For example, the number of users of online social networking platforms such as Twitter, Facebook, and Sina Weibo has grown rapidly in recent years. While social networks bring convenience to people's lives, they also attract a large number of spam messages (Spam) and spammers (Spammer) because of their unique fission-style communication patterns. For example, spam information such as fake news, fake lottery winning information, and ille...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F17/30G06K9/62G06Q50/00
CPCG06F16/951G06Q50/01G06F18/2155G06F18/24
Inventor 徐光侠赵竞腾齐锦刘宴兵黄德玲赵璐李培真代皓张令浩
Owner CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products