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      KCI등재 SCIE SCOPUS

      A Method of Finding Hidden Key Users Based on Transfer Entropy in Microblog Network = A Method of Finding Hidden Key Users Based on Transfer Entropy in Microblog Network

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      https://www.riss.kr/link?id=A107040160

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      다국어 초록 (Multilingual Abstract)

      Finding key users in microblog has been a research hotspot in recent years. There are two kinds of key users: obvious and hidden ones. Influence of the former is direct while that of the latter is indirect. Most of existing methods evaluate user's direct influence, so key users they can find usually obvious ones, and their ability to identify hidden key users is very low as hidden ones exert influence in a very covert way. Consequently, the algorithm of finding hidden key users based on topic transfer entropy, called TTE, is proposed. TTE algorithm believes that hidden key users are those normal users possessing a high covert influence on obvious ones. Firstly, obvious key users are discovered based on microblog propagation scale. Then, based on microblogs' topic similarity and time correlation, the transfer entropy from ordinary users' blogs to obvious key users is calculated and used to measure the covert influence. Finally, hidden influence degrees of ordinary users are comprehensively evaluated by combining above indicators with the influence of both ordinary users and obvious ones. We conducted experiments on Sina Weibo, and the results showed that TTE algorithm had a good ability to identify hidden key users.
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      Finding key users in microblog has been a research hotspot in recent years. There are two kinds of key users: obvious and hidden ones. Influence of the former is direct while that of the latter is indirect. Most of existing methods evaluate user's dir...

      Finding key users in microblog has been a research hotspot in recent years. There are two kinds of key users: obvious and hidden ones. Influence of the former is direct while that of the latter is indirect. Most of existing methods evaluate user's direct influence, so key users they can find usually obvious ones, and their ability to identify hidden key users is very low as hidden ones exert influence in a very covert way. Consequently, the algorithm of finding hidden key users based on topic transfer entropy, called TTE, is proposed. TTE algorithm believes that hidden key users are those normal users possessing a high covert influence on obvious ones. Firstly, obvious key users are discovered based on microblog propagation scale. Then, based on microblogs' topic similarity and time correlation, the transfer entropy from ordinary users' blogs to obvious key users is calculated and used to measure the covert influence. Finally, hidden influence degrees of ordinary users are comprehensively evaluated by combining above indicators with the influence of both ordinary users and obvious ones. We conducted experiments on Sina Weibo, and the results showed that TTE algorithm had a good ability to identify hidden key users.

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      참고문헌 (Reference)

      1 H. He, "Tree-Based Mining for Discovering Patterns of Re-posting Behavior in microblog" 372-384, 2013

      2 Bo Xiao, "SMK-means : An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data" 56 (56): 365-379, 2018

      3 I. Cohen, "Pearson Correlation Coefficient" 2 : 1-4, 2009

      4 Z. Y. Ding, "Measuring the spreadability of users in microblogs" 14 (14): 701-710, 2013

      5 T. Schreiber, "Measuring information transfer" 85 (85): 461-464, 2000

      6 D. Kempe, "Maximizing the spread of influence through a social network" 137-146, 2003

      7 D. M. Blei, "Latent dirichlet allocation" 993 (993): 993-1022, 2003

      8 S. C. Johnson, "Hierarchical clustering schemes" 32 (32): 241-254, 1967

      9 P. P. Rodrigues, "Hierarchical Clustering of Time-Series Data Streams" 20 (20): 615-627, 2008

      10 L. Barnett, "Granger causality and transfer entropy are equivalent for Gaussian variablesc" 103 (103): 238701-238701, 2009

      1 H. He, "Tree-Based Mining for Discovering Patterns of Re-posting Behavior in microblog" 372-384, 2013

      2 Bo Xiao, "SMK-means : An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data" 56 (56): 365-379, 2018

      3 I. Cohen, "Pearson Correlation Coefficient" 2 : 1-4, 2009

      4 Z. Y. Ding, "Measuring the spreadability of users in microblogs" 14 (14): 701-710, 2013

      5 T. Schreiber, "Measuring information transfer" 85 (85): 461-464, 2000

      6 D. Kempe, "Maximizing the spread of influence through a social network" 137-146, 2003

      7 D. M. Blei, "Latent dirichlet allocation" 993 (993): 993-1022, 2003

      8 S. C. Johnson, "Hierarchical clustering schemes" 32 (32): 241-254, 1967

      9 P. P. Rodrigues, "Hierarchical Clustering of Time-Series Data Streams" 20 (20): 615-627, 2008

      10 L. Barnett, "Granger causality and transfer entropy are equivalent for Gaussian variablesc" 103 (103): 238701-238701, 2009

      11 Seth A, "Granger causality" 2 (2): 1667-1667, 2007

      12 T. L. Griffiths, "Finding scientific topics" 101 (101): 5228-5235, 2004

      13 Di Shang, "Examining the Impacts of Key Influencers on Community Development" 61 (61): 1-10, 2019

      14 E. Bakshy E, "Everyone's an influencer : quantify-ing influence on twitter" 65-74, 2011

      15 Kai Dong, "Estimating the Number of Posts in Sina Weibo" 58 (58): 197-213, 2019

      16 Zheng Yongguang, "Efficient key user selection method in large-scale social networks" 37 (37): 3101-3106, 2017

      17 Ziqi Tang, "Disseminating Quality-Based Analysis of microblog Users' Influencing Ability" 499-514, 2018

      18 T. Bossomaier, "An Introduction to Trans-fer Entropy" 65-95, 2016

      19 Yong Hua, "An Influence Maximization Algorithm Based on the Mixed Importance of Nodes" 59 (59): 517-531, 2019

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      학술지등록 한글명 : KSII Transactions on Internet and Information Systems
      외국어명 : KSII Transactions on Internet and Information Systems
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2013-10-01 평가 등재학술지 선정 (기타) KCI등재
      2011-01-01 평가 등재후보학술지 유지 (기타) KCI등재후보
      2009-01-01 평가 SCOPUS 등재 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.45 0.21 0.37
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.32 0.29 0.244 0.03
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