Journal of Information Processing Systems

  • 제13권4호
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  • Pages.987-999
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  • 2017
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Identifying Influential People Based on Interaction Strength

  • Zia, Muhammad Azam (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications) ;
  • Zhang, Zhongbao (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications) ;
  • Chen, Liutong (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications) ;
  • Ahmad, Haseeb (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications) ;
  • Su, Sen (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications)
  • 투고 : 2017.04.21
  • 심사 : 2017.06.21
  • 발행 : 2017.08.31
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초록

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

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