Social relation modeling method based on moving track

A technology of social relations and movement trajectory, applied in the field of social networking

Active Publication Date: 2019-10-25
HUAZHONG AGRI UNIV
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods have certain limitations. First, because in reality, the relationship strength between mobile nodes is not only related to the number of encounters, but also related to the duration of each encounter. The ch

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
  • Social relation modeling method based on moving track
  • Social relation modeling method based on moving track
  • Social relation modeling method based on moving track

Examples

Experimental program
Comparison scheme
Effect test

example

[0089] 1. Experimental data

[0090] The Geolife project used in this experiment contains the GPS trajectory data set of 182 nodes within five years (April 2007 to August 2012). The GPS tracks of this dataset are represented by a sequence of time-stamped record points, each time point containing information about latitude, longitude, and altitude. The entire dataset contains 17621 trajectories with a total distance of 12.9251 million km and a total duration of 50176 hours. These traces are produced by different GPS loggers and GPS phones, and have various sampling rates. 91.5% of the trajectories are recorded in a dense representation, for example, the trajectories are recorded every 1-5 seconds or 5-10 meters per point, forming a sequence of time stamps.

[0091] 2 Results and Analysis

[0092] 2.1 Results of community division based on trajectory data

[0093] The user social relationship network constructed based on the trajectory data of 182 users is as follows: Figu...

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 social relation modeling method based on a moving track, and the method comprises the following steps: 1), recording track points of a user according to a preset time interval in a set time period according to the collected track data of the user, and forming a timestamp sequence of a user track; 2) judging whether the users meet according to the timestamp sequence of each user track, and if so, recording a meeting record vector between every two users; 3) constructing a social relation model based on the movement track according to the encountering record vector; and4) calculating the social relationship strength among the users according to the social relationship model, and dividing the mobile communities to which the users belong. According to the method, thesocial relation intensity is measured by calculating the encountering times and the encountering time between different mobile users, the social relation intensity is converted into the weighted social relation network, and therefore different user communities are constructed, and help is provided for more accurate position service based on tracks.

Description

technical field [0001] The invention relates to social network technology, in particular to a method for modeling social relations based on moving tracks. Background technique [0002] With the maturity of portable devices and positioning technology, the amount of trajectory data is increasing. Large-scale trajectory data depicts the spatiotemporal dynamics of individuals and groups, and contains behavioral information of humans, vehicles, and animals. How to extract potential and meaningful knowledge from them has become a new research hotspot and problem in the field of data mining. The location prediction of mobile users is the premise of providing location-based services, and the social relationship of mobile users hidden in trajectory data is potentially useful information, which can improve the accuracy of mobile pattern mining and mobile location prediction. How to measure the strength of social relations between people in a social network has always been a difficult...

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): G06F17/50G06Q50/00
CPCG06Q50/01G06F30/20
Inventor 赵良
Owner HUAZHONG AGRI UNIV
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
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap