Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Message propagation prediction method and device based on online social relation network

A social relationship and communication prediction technology, applied in the fields of social media and big data, can solve problems such as the difficulty in dealing with the effective modeling of information diffusion laws and the influence of information dissemination, and achieve the effect of high accuracy and flexible form

Inactive Publication Date: 2017-03-15
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
View PDF2 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This kind of strong interaction and strong evolution makes a large amount of information in the online social network spread and spread rapidly in the form of flow, and the dissemination of information is always in a state of unstable emergence from content distribution to influence coverage. It is difficult to deal with the effective modeling of the law of information diffusion and the objective measurement of the influence of information dissemination with research methods

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
  • Message propagation prediction method and device based on online social relation network
  • Message propagation prediction method and device based on online social relation network
  • Message propagation prediction method and device based on online social relation network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The inventors found that the online social relationship network is a multi-dimensional complex system when conducting research on the social relationship network, and the subsystems inside the network frequently influence and interact with each other. In this system, the behavior of user groups generally has the characteristics of gradually accumulating and erupting overnight, and such accumulation is not linear, and cannot be solved by simple superposition. It is often a series of small changes, each of which is insufficient. can affect the overall system, but when a critical state is reached, the entire system undergoes a critical phase transition, which we call the emergence phenomenon. In the social information network, the multi-source heterogeneous network information interacts at different levels, so that the properties of the entire network cannot be superimposed by the properties of the subsystems, and the emerging properties of the overall system cannot be desc...

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 proposes a message propagation prediction method and device based on an online social relation network, and relates to the technical field of social media and big data. The method comprises the steps: 1, obtaining the time sequence of following behavior arrival time of users in a time period [0, T] for a message d after the sending of the message d; 2, modeling the time sequence, learning the model generated through modeling, training the parameters of the model, and obtaining a message popularity prediction function according to the model parameters. The method can avoid a data surge phenomenon. Compared with MAPE, the method is higher in accuracy, is flexible in mode, and can be used in other scenes.

Description

technical field [0001] The invention relates to the technical fields of social media and big data, in particular to a method and device for predicting message propagation based on an online social relationship network. Background technique [0002] In recent years, with the rapid development of social networking online services such as social networks, social media, and e-commerce, people have accumulated a large amount of large-scale graph data, and the number of registered users of Sina Weibo has exceeded 600 million. Nearly 100 million microblogs are sent every day, and microblogs are spread along the following relationship among users, forming a spreading network. Business departments facing network big data and business systems based on network big data are also collected and accumulated. Massive graph data, for the storage, organization, analysis and processing of massive graph data, has become a technical bottleneck and business key for in-depth analysis and effective...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/00
CPCG06Q10/04G06Q50/01
Inventor 赵志云刘春阳李雄张旭庞琳何扬王萌王卿张静乐沈华伟
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com