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Ranking scientific publications considering the aging characteristics of citations

Author

Listed:
  • Yanan Wang

    (Beijing Normal University)

  • An Zeng

    (Beijing Normal University)

  • Ying Fan

    (Beijing Normal University)

  • Zengru Di

    (Beijing Normal University)

Abstract

Ranking the significance of scientific publications has been a challenging topic for a long time. So far, many ranking methods have been proposed, one of which is the well-known PageRank algorithm. In this paper, we introduce aging characteristics to the PageRank algorithm via considering only the first 10 year citations when aggregating resource from different nodes. The validation of our new method is performed on the data of American Physical Society journals. The results indicate that taking into account aging characteristics improves the performance of the PageRank algorithm in terms of ranking accuracy for both papers and authors. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems and similar improvements are expected.

Suggested Citation

  • Yanan Wang & An Zeng & Ying Fan & Zengru Di, 2019. "Ranking scientific publications considering the aging characteristics of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 155-166, July.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:1:d:10.1007_s11192-019-03117-9
    DOI: 10.1007/s11192-019-03117-9
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    References listed on IDEAS

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    Cited by:

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    3. Alexander Kuchansky & Andrii Biloshchytskyi & Yurii Andrashko & Svitlana Biloshchytska & Adil Faizullin, 2022. "The Scientific Productivity of Collective Subjects Based on the Time-Weighted PageRank Method with Citation Intensity," Publications, MDPI, vol. 10(4), pages 1-17, October.
    4. 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.
    5. Wang, Jingjing & Xu, Shuqi & Mariani, Manuel S. & Lü, Linyuan, 2021. "The local structure of citation networks uncovers expert-selected milestone papers," Journal of Informetrics, Elsevier, vol. 15(4).
    6. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Gerson Pech & Catarina Delgado, 2020. "Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 223-252, April.

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