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Age preference of metrics for identifying significant nodes in growing citation networks

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  • Ren, Zhuo-Ming

Abstract

Identifying significant works in the field of arts and sciences should avoid age preference. Recent research has shown that the time-balanced variant of the popular Google’s PageRank and degree for identifying significant works could provide an objective approach to eliminate the age preference in growing networks. However, a fundamental question remains open: How much performance capability do time-balanced metrics expect when they identify significant nodes in the growing networks? Through investigating of two large time-aggregated citations networks between movies procured from the Internet Movie Database and papers published on the journals of American Physical Society respectively, we analyze the age preference of several time-balanced metrics of PageRank and degree for identifying significant nodes in comparison.

Suggested Citation

  • Ren, Zhuo-Ming, 2019. "Age preference of metrics for identifying significant nodes in growing citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 325-332.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:325-332
    DOI: 10.1016/j.physa.2018.09.001
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    References listed on IDEAS

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    1. Max Wasserman & Satyam Mukherjee & Konner Scott & Xiao Han T. Zeng & Filippo Radicchi & Luís A. N. Amaral, 2015. "Correlations between user voting data, budget, and box office for films in the internet movie database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(4), pages 858-868, April.
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    5. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.
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    Cited by:

    1. Xiang Li & Chengli Zhao & Zhaolong Hu & Caixia Yu & Xiaojun Duan, 2022. "Revealing the character of journals in higher-order citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6315-6338, November.
    2. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    3. Monachary Kammari & Durga Bhavani S, 2023. "Time-stamp based network evolution model for citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3723-3741, June.
    4. 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).
    5. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).

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