A Social Networks User Relationship Strength Model Based on Hawkes Process

doi:10.3969/j.issn.0372-2112.2016.06.015

您当前的位置：

首页 >

文章列表页 >

A Social Networks User Relationship Strength Model Based on Hawkes Process

更新时间：2025-07-02

- A Social Networks User Relationship Strength Model Based on Hawkes Process
- Acta Electronica Sinica Vol. 44, Issue 6, Pages: 1362-1368(2016)
- 作者机构：
  
  国家数字交换系统工程技术研究中心,河南,郑州,450002
- 作者简介：
- 基金信息：
  
  National Key Technology Research and Development Program of the Ministry of Science and Technology in 2014 (No.2014BAH30B01)
- DOI：10.3969/j.issn.0372-2112.2016.06.015
  CLC： TP393
- Published Online：25 June 2016，
  
  Published：2016
- 稿件说明：
移动端阅览
A Social Networks User Relationship Strength Model Based on Hawkes Process[J]. Acta Electronica Sinica, 2016, 44(6): 1362-1368.
DOI：

A Social Networks User Relationship Strength Model Based on Hawkes Process[J]. Acta Electronica Sinica, 2016, 44(6): 1362-1368. DOI： 10.3969/j.issn.0372-2112.2016.06.015.

摘要

社交网络节点之间的关系强度建模是研究信息传播、实现推荐服务等社交网络服务的关键.传统关系强度模型主要研究简单二元关系与静态关系

未考虑用户交互影响及其动态衰减.本文提出一种基于霍克斯过程的社交网络用户关系强度模型

将用户关系强度视为潜在因子

用户相似性与历史交互行为分别视为潜在因子诱因与表象

并使用霍克斯过程刻画历史交互行为与用户关系强度之间的关系

解决了已有模型未考虑用户历史交互影响及其动态衰减的问题.采用微博社交网络数据对模型进行的评估表明

本模型可以提高用户关系强度预测精度以及基于关系强度排序Top-

邻居节点的覆盖率.

Abstract

The relationship strength model between social network nodes is the key of social networks service such as information dissemination researches and recommendation system.The traditional researches focus on modeling simple binary relations and static relations

without considering dynamic attenuation of user interaction effects.Aiming at this problem

this paper proposes a social networks user relationship strength model based on Hawkes process(HP-URS)

which takes the relationship strength

similarity and history interaction behavior between users as a latent factor

latent factor incentive and presentation respectively.This model uses Hawkes process to characterize relationship between history interaction behavior and user relations

hip strength.This model provides a solution of the disadvantages of the original model without considering user history interaction effects and their attenuation.This paper uses the data from microblog social networks evaluating HP-URS model

and the experimental results show that this model can improve relationship strength prediction accuracy and coverage rate of the Top-

neighbor nodes based on relationship strength.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Influence Measure of Nodes Based on Structures of Social Networks

A Multi-Order Neighbor Based Multi-Label Classification Algorithm in Network Environments

A Self-Adaptive Microblog Topic Tracking Method by User Relationship

Personalized Microblogging Recommendation Based on Dynamic Interests and Social Networking of Users

Microblog Bursty Events Detection Method Based on Multiple Word Features

Related Author

WANG Hong-yu

ZUO Yang

LI Ze-peng

WANG Meng

LI Lei

ZHANG Zan

WANG Hao

ZHANG Chuang

Related Institution

School of Electronics Engineering and Computer Science Peking University Beijing China

Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education Beijing China

School of Electronics Engineering and Computer Science, Peking University

Key Laboratory of High Confidence Software Technologies(Peking University), Ministry of Education

School of Computer and Information, Hefei University of Technology

⁰