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P‐Rank: An indicator measuring prestige in heterogeneous scholarly networks

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  • Erjia Yan
  • Ying Ding
  • Cassidy R. Sugimoto

Abstract

Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P‐Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P‐Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P‐Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P‐Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.

Suggested Citation

  • Erjia Yan & Ying Ding & Cassidy R. Sugimoto, 2011. "P‐Rank: An indicator measuring prestige in heterogeneous scholarly networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(3), pages 467-477, March.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:3:p:467-477
    DOI: 10.1002/asi.21461
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    Cited by:

    1. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    3. Yuanyuan Liu & Qiang Wu & Shijie Wu & Yong Gao, 2021. "Weighted citation based on ranking-related contribution: a new index for evaluating article impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8653-8672, October.
    4. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    5. Wang, Ruby W. & Wei, Shelia X. & Ye, Fred Y., 2021. "Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength," Journal of Informetrics, Elsevier, vol. 15(3).
    6. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.
    7. Geraldo J. Pessoa Junior & Thiago M. R. Dias & Thiago H. P. Silva & Alberto H. F. Laender, 2020. "On interdisciplinary collaborations in scientific coauthorship networks: the case of the Brazilian community," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2341-2360, September.
    8. Amodio, Pierluigi & Brugnano, Luigi & Scarselli, Filippo, 2021. "Implementation of the PaperRank and AuthorRank indices in the Scopus database," Journal of Informetrics, Elsevier, vol. 15(4).

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