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

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Abstract

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.

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Acknowledgements

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).

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei, 230027, China

    Kai Li, Guangyi Lv, Zhefeng Wang, Qi Liu, Enhong Chen & Lisheng Qiao

Authors
  1. Kai Li
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  2. Guangyi Lv
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  3. Zhefeng Wang
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  4. Qi Liu
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  5. Enhong Chen
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  6. Lisheng Qiao
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Corresponding author

Correspondence to Enhong Chen.

Additional information

Kai Li received the BE degree in Computer Science in 2000 from Yanshan University, China and the MS degree of Computer Science from Jilin University, China in 2003. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China, China. His major research interests include social networks, human dynamics and machine learning.

Guangyi Lv received the BE degree in Computer Science and Technology in 2013 from Sichuan University, China. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China, China. His major research interests include deep learning, natural language processing and recommendation system. He has published several papers in refereed conference proceedings, such as AAAI’16, AAAI’17, PAKDD’15.

Zhefeng Wang received the BE degree in Computer Science in University of Science and Technology of China, China. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. His major research interests include social network, social media analysis, machine learning, and text mining. He has published several papers in refereed conference proceedings and journals such as SIGIR, KDD, IJCAI, and TKDE.

Qi Liu is an associate professor in University of Science and Technology of China (USTC). He received his PhD in Computer Science from USTC. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings, e.g., TKDE, TOIS, TKDD, TIST, KDD, IJCAI, AAAI, ICDM, SDM, and CIKM. He has served regularly in the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He is a member of ACM and IEEE. Dr. Liu is the recipient of the ICDM 2011 Best Research Paper Award, the Best of SDM 2015 Award, the Special Prize of President Scholarship for Postgraduate Students, Chinese Academy of Sciences (CAS) and the Distinguished Doctoral Dissertation Award of CAS.

Enhong Chen is a professor and vice dean of the School of Computer Science at USTC. He received the PhD degree from USTC. His general area of research includes data mining and machine learning, social network analysis and recommender systems. He has published more than 100 papers in refereed conferences and journals, including IEEE Trans. KDE, IEEE Trans. MC, KDD, ICDM, NIPS, and CIKM. He was on program committees of numerous conferences including KDD, ICDM, and SDM. His research is supported by the National Science Foundation for Distinguished Young Scholars of China. He is a senior member of the IEEE.

Lisheng Qiao received the BE degree in electric power system & automation in 2009 from Southwest Jiao Tong University, China. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China, China. His major research interests include deep learning, natural language processing, and social network analysis.

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Li, K., Lv, G., Wang, Z. et al. Understanding the mechanism of social tie in the propagation process of social network with communication channel. Front. Comput. Sci. 13, 1296–1308 (2019). https://doi.org/10.1007/s11704-018-7453-x

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