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ConformRank: A conformity-based rank for finding top-k influential users

Author

Listed:
  • Wang, Qiyao
  • Jin, Yuehui
  • Cheng, Shiduan
  • Yang, Tan

Abstract

Finding influential users is a hot topic in social networks. For example, advertisers identify influential users to make a successful campaign. Retweeters forward messages from original users, who originally publish messages. This action is referred to as retweeting. Retweeting behaviors generate influence. Original users have influence on retweeters. Whether retweeters keep the same sentiment as original users is taken into consideration in this study. Influence is calculated based on conformity from emotional perspective after retweeting. A conformity-based algorithm, called ConformRank, is proposed to find top-k influential users, who make the most users keep the same sentiment after retweeting messages. Emotional conformity is introduced to denote how users conform to original users from the emotional perspective. Conforming weights are introduced to denote how two users keep the same sentiment after retweeting messages. Emotional conformity is applied for users and conforming weights are used for relations. Experiments were conducted on Sina Weibo. Experimental results show that users have larger influence when they publish positive messages.

Suggested Citation

  • Wang, Qiyao & Jin, Yuehui & Cheng, Shiduan & Yang, Tan, 2017. "ConformRank: A conformity-based rank for finding top-k influential users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 39-48.
    • Handle: RePEc:eee:phsmap:v:474:y:2017:i:c:p:39-48
    DOI: 10.1016/j.physa.2016.12.040
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    References listed on IDEAS

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    1. Bo Zhang & Yufeng Wang & Qun Jin & Jianhua Ma, 2015. "A Pagerank-Inspired Heuristic Scheme for Influence Maximization in Social Networks," International Journal of Web Services Research (IJWSR), IGI Global, vol. 12(4), pages 48-62, October.
    2. Eom, Young-Ho & Shepelyansky, Dima L., 2015. "Opinion formation driven by PageRank node influence on directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 707-715.
    3. Wang, Qiyao & Jin, Yuehui & Lin, Zhen & Cheng, Shiduan & Yang, Tan, 2016. "Influence maximization in social networks under an independent cascade-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 20-34.
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    Cited by:

    1. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.
    2. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.

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