Social network information diffusion model based on user’s influence and interesting

Rui WANG; Yong LIU; Jing-hua ZHU; Ping XUAN; Jin-bao LI

doi:10.11959/j.issn.1000-436x.2017264

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Social network information diffusion model based on user’s influence and interesting

Papers | 更新时间：2024-06-05

- Social network information diffusion model based on user’s influence and interesting
- Journal on Communications Vol. 38, Issue Z2, Pages: 113-121(2017)
- 作者机构：
  
  1. 黑龙江大学计算机科学技术学院，黑龙江哈尔滨 150080
  2. 黑龙江省数据库与并行计算重点实验室，黑龙江哈尔滨 150080
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(6137022);The National Natural Science Foundation of China(61602159);The National Natural Science Foundation of China(61300225);The Natural Science Foundation of Heilongjiang Province(F201430);The Natural Science Foundation of Heilongjiang Province(F2015013);The Innovation Talents Project of Science and Technology Bureau of Harbin(2017RAQXJ094);The Innovation Talents Project of Science and Technology Bureau of Harbin(2015RAQXJ004);The Program for Group of Science Harbin Technological Innovation Found(2015RAXXJ004);The Fundamental Research Funds of Universities in Heilongjiang Province(HDJCCX-201608)
- DOI：10.11959/j.issn.1000-436x.2017264
  CLC： TP311
- Online First：2017-11，
  
  Published：25 November 2017
- 稿件说明：
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Rui WANG, Yong LIU, Jing-hua ZHU, et al. Social network information diffusion model based on user’s influence and interesting[J]. Journal on Communications, 2017, 38(Z2): 113-121.
DOI：

Rui WANG, Yong LIU, Jing-hua ZHU, et al. Social network information diffusion model based on user’s influence and interesting[J]. Journal on Communications, 2017, 38(Z2): 113-121. DOI： 10.11959/j.issn.1000-436x.2017264.

摘要

提出了一种新的无拓扑结构的社交网信息传播模型，简称 NT-II，并使用表达学习方式，构建了 2 个隐藏的空间：用户影响空间和用户兴趣空间，每个用户和每个传播项都映射成空间中的向量。模型在预测用户接收传播项的概率时，既考虑来自其他用户的影响程度，又考虑该用户对传播项的喜爱程度，分别根据2个用户向量之间的距离和用户向量和传播项向量之间的距离来推断。实验结果表明：NT-II 模型能更准确地模拟传播过程和预测传播结果。

Abstract

A new non-topological information diffusion model of social network was proposed

called non-topological influence-interest diffusion model (NT-II).Representation learning was exploited to construct two hidden spaces for NT-II，called the user-influence space and the user-interest space

each user and each propagation item was mapped into a vector in space.The model predicted the probability of a user receiving a propagated item

considering not only the degree of influence from other users

but also the user's preference for propagated item.The experimental results show that the model can simulate the propagation process and predict the propagation results more accurately.

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references

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