Frontiers of Computer Science

, Volume 13, Issue 6, pp 1296–1308 | Cite as

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

  • Kai Li
  • Guangyi Lv
  • Zhefeng Wang
  • Qi Liu
  • Enhong ChenEmail author
  • Lisheng Qiao
Research Article


The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers 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 neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, 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 neighbor. To 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. The 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 tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.


information propagation social networks mechanism of social tie communication channel 


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The authors thank Biao Chang for his valuable suggestions. This research was partially supported by the National Natural Science Foundation of China (Grants Nos. U1605251, 61727809 and 91546110), the Youth Innovation Promotion Association of CAS (2014299), and Special Program for Applied Research on Super Computation of the NSFCGuangdong Joint Fund (the second phase).

Supplementary material

11704_2018_7453_MOESM1_ESM.ppt (189 kb)
Supplementary material, approximately 189 KB.


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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Kai Li
    • 1
  • Guangyi Lv
    • 1
  • Zhefeng Wang
    • 1
  • Qi Liu
    • 1
  • Enhong Chen
    • 1
    Email author
  • Lisheng Qiao
    • 1
  1. 1.Anhui Province Key Laboratory of Big Data Analysis and ApplicationUniversity of Science and Technology of ChinaHefeiChina

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