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Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Author

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  • Yufei Liu
  • Dechang Pi
  • Lin Cui

Abstract

Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

Suggested Citation

  • Yufei Liu & Dechang Pi & Lin Cui, 2017. "Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo," Complexity, Hindawi, vol. 2017, pages 1-16, December.
    • Handle: RePEc:hin:complx:4783159
    DOI: 10.1155/2017/4783159
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    References listed on IDEAS

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    1. Yan, Qiang & Wu, Lianren & Zheng, Lan, 2013. "Social network based microblog user behavior analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1712-1723.
    2. Gabriele Lohmann & Daniel S Margulies & Annette Horstmann & Burkhard Pleger & Joeran Lepsien & Dirk Goldhahn & Haiko Schloegl & Michael Stumvoll & Arno Villringer & Robert Turner, 2010. "Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-8, April.
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    Cited by:

    1. Susu Yang & Hui Li & Zhongyuan Jiang, 2018. "Targeted Influential Nodes Selection in Location-Aware Social Networks," Complexity, Hindawi, vol. 2018, pages 1-10, November.

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