Information tracing model based on PageRank

Qian LI; Jia-wei LAI; Yun-peng XIAO; Bin WU

doi:10.11959/j.issn.2096-109x.2017.00190

您当前的位置：

首页 >

文章列表页 >

Information tracing model based on PageRank

Papers | 更新时间：2024-06-03

- Information tracing model based on PageRank
- Chinese Journal of Network and Information Security Vol. 3, Issue 8, Pages: 68-76(2017)
- 作者机构：
  
  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia,Beijing University of Posts and Telecommunications,Beijing 100876,China
  2. Chongqing Engineering Laboratory of Network and Information Security,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2017.00190
  CLC：
- Online First：2017-08，
  
  Published：15 August 2017
- 稿件说明：
移动端阅览
Qian LI, Jia-wei LAI, Yun-peng XIAO, et al. [J]. Chinese Journal of Network and Information Security, 2017, 3(8): 68-76.
DOI：

Qian LI, Jia-wei LAI, Yun-peng XIAO, et al. [J]. Chinese Journal of Network and Information Security, 2017, 3(8): 68-76. DOI： 10.11959/j.issn.2096-109x.2017.00190.

摘要

Abstract

In social network

original publisher and important nodes in the diffusion process can be traced by analyzing the spreading network of a hot topic.The participated users and spreading network structure of a hot topic build an information tracing model

which mines the source and important diffusion nodes.Firstly

it analyzed the development trend of a hot topic and extracts the users involved.Secondly

it established a user network according to the following relationship of the users involved.Thirdly

the contribution rate of users on the development of the hot topic was initialized

and the PageRank algorithm was used to construct the information tracing model.Finally

the Top k users were selected as the information publisher and important users of the hot topic according to the contribution rate.Experimental results showed that our model can effectively discover the hot topic of the publisher and important users.

关键词

Keywords

references

OPSAHL T , AGNEESSENS F , SKVORETZ J . Node centrality in weighted networks:generalizing degree and shortest paths [J ] . Social networks , 2010 , 32 : 245 - 251 .

OLSEN P W , LABOUSEUR A G , HWAN J H . Efficient top-k closeness centrality search [C ] // Data Engineering (ICDE) . 2014 : 196 - 207 .

RIONDATO M , KORNAROPOULOS E M . Fast approximation of betweenness centrality through sampling [J ] . Data Mining and Knowledge Discovery,New York , 2016 , 30 : 438 - 475 .

MORONE F , MAKSE H A . Influence maximization in complex networks through optimal percolation [J ] . Nature , 2015 , 524 : 65 - 68 .

AKOGLU L , TONG H , KOUTRA D . Graph based anomaly detection and description:a survey [J ] . Data Mining and Knowledge Discovery , 2015 , 29 : 626 - 688 .

BAO J , ZHENG Y , WILKIE D , et al . Recommendations in location-based social networks:a survey [J ] . GeoInformatica , 2015 , 19 : 525 - 565 .

MA N , LIU Y . SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork [J ] . Expert Systems with Applications , 2014 ( 3 ): 1357 - 1368 .

ALEAHMAD A , KARISANI P , RAHGOZAR M , et al . OLFinder:Finding opinion leaders in online social networks [J ] . Journal of Information Science , 2015 , 42 ( 9 ): 659 - 674 .

ROMERO D M , GALUBA W , ASUR S , et al . Influence and Passivity in Social Media [C ] // Joint European Conference on Machine Learning and Knowledge Discovery in Databases . 2011 : 18 - 33 .

WENG J S , LIM P E , JIANG J , et al . Twitterrank:finding topic-sensitive influential twitterers [C ] // The third ACM international conference on Web search and data mining . 2011 : 261 - 270 .

TANG J , SUN J , WANG C , et al . Social influence analysis in large-scale network [C ] // The 15th ACM SIGKDD international conference on Knowledge discovery and data mining . 2009 : 807 - 816 .

ANTULOV-FANTULIN N , LANCIC A , STEFANCIC H , et al . Statistical inference framework for source detection of contagion processes on arbitrary network structures [C ] // Self-Adaptive and Self-Organizing Systems Workshops (SASOW) . 2014 : 78 - 83 .

JIANG J J , WEN S , YU S , et al . Identifying propagation sources in networks:State-of-the-art and comparative studies [C ] // IEEE Communications Surveys ＆ Tutorials . 2016 : 165 - 481

ALTARELLI F , BRAUNSTEIN A , DALL’ASTA L , et al . The patient-zero problem with noisy observations [J ] . Journal of Statistical Mechanics:Theory and Experiment , 2014 ( 10 ).

Views

844

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰