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Identifying Influential Users by Improving LeaderRank

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1075))

Abstract

In large-scale social network, influential users play important roles in public opinion analysis and information promotion. So, identifying influential users attract increasing attention in social network studies, and researchers have been trying to find a better algorithm to identify influential users. PageRank is useful for searching information in World Wide Web, but it has less effectiveness in social networks. LeaderRank is improved by PageRank and it has better ranking effectiveness than PageRank. But LeaderRank doesn’t consider the impact of the various personal attributes of the users. Therefore, this thesis proposes a new algorithm, the ANiceRank, which combines with the users’ multiple properties. To test the effectiveness of our algorithms, we introduce SIR model, which is widely used in virus transmission and information dissemination in social networks. The results show that the accuracy of the improved algorithm has been effectively improved and it can be used to identify influential users in social networks.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Shaanxi, China

    Yong Yao & Cong Ji

Authors
  1. Yong Yao
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  2. Cong Ji
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Corresponding author

Correspondence to Cong Ji .

Editor information

Editors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Yao, Y., Ji, C. (2020). Identifying Influential Users by Improving LeaderRank. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_49

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