Mining Topical Influencers Based on the Multi-Relational Network in Micro-Blogging Sites

作     者：丁兆云 贾焰 周斌 韩毅 

作者机构：School of Computer National University of Defense Technology 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2013年第10卷第1期

页      面：93-104页

核心收录：

学科分类：0828[工学-农业工程] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 

基　　金：supported by National Natural Science Foundation of China under Grants No. 60933005, No. 91124002 under Grants No. 012505, No. 2011AA010702, No. 2012AA01A401, No. 2012AA01A402 (863 program) under Grant No.2011A010 (242) NSTM under Grants No.2012BAH38B04, No.2012BAH38B06 

主　　题：social network topical influence pagerank multi-relational network influencers micro-blogging 

摘      要：In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given *** many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given *** this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and *** to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational ***,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and *** were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.