Understanding the mechanism of social tie in the propagation process of social network with communication channel

作     者：Kai LI Guangyi LV Zhefeng WANG Qi LIU Enhong CHEN Lisheng QIAO 

作者机构：Anhui Province Key Laboratory of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefei 230027China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2019年第13卷第6期

页      面：1296-1308页

核心收录：

学科分类：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 the National Natural Science Foundation of China(Grants Nos.U1605251,61727809 and 91546110) the Youth Innovation Promotion Association of CAS(2014299) Special Program for Applied Research on Super Computation of the NSFCGuangdong Joint Fund(the second phase) 

主　　题：information propagation social networks mechanism of social tie communication channel 

摘      要：The propagation of information in online social networks plays a critical role in modern life,and thus has been studied *** have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his ***,the research on the mechanism how social ties between users play roles in propagation process is still ***,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a *** this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)*** model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social *** experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.