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UR Rank: Micro-blog User Influence Ranking Algorithm Based on User Relationship

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Book cover Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2017)

Abstract

In this paper, a novel UR Rank (User Relationships based Ranking) algorithm is proposed for ranking the influence of the user. We first explore five factors that affect user relationship. They are following rate (FR) factor, activity (ACT) factor, authority (ATR) factor, interaction (ITA) factor and similarity (SML) factor. Then those factors are used in Support Vector Regression (SVR) model to predict the relationship between users. We assimilate such predicted relationship into a PageRank based transition probability to identify influential users. The experiments on a real micro-blog data set demonstrate that UR Rank algorithm has better performance and is more persuasive than the existing algorithms.

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Acknowledgements

This work was partly supported by the NSFC-Guangdong Joint Found (U1501254) and the Co-construction Program with the Beijing Municipal Commission of Education and the Ministry of Science and Technology of China (2012BAH45B01) and National key research and development program (2016YFB0800302) the Director’s Project Fund of Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education (Grant No. 2017ZR01) and the Fundamental Research Funds for the Central Universities (BUPT2011RCZJ16, 2014ZD03-03) and China Information Security Special Fund (NDRC).

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Authors and Affiliations

  1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China

    Wenbin Yao & Yiwei Yang

  2. National Engineering Laboratory for Mobile Network Security, Beijing University of Posts and Telecommunications, Beijing, China

    Wenbin Yao & Yiwei Yang

  3. Key Laboratory of Ministry of Education for Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing, China

    Dongbin Wang

Authors
  1. Wenbin Yao
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  2. Yiwei Yang
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  3. Dongbin Wang
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Corresponding author

Correspondence to Yiwei Yang .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

  2. Guangdong University of Petrochemical Technology, Maoming, China

    Lei Shu

  3. Osaka University, Osaka, Japan

    Hara Takahiro

  4. China University of Geosciences, Beijing, China

    Zhangbing Zhou

  5. University of Nebraska–Lincoln, Lincoln, UK

    Timothy Gordon

  6. School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

    Deze Zeng

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yao, W., Yang, Y., Wang, D. (2018). UR Rank: Micro-blog User Influence Ranking Algorithm Based on User Relationship. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-00916-8_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00915-1

  • Online ISBN: 978-3-030-00916-8

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