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Ranking scientific publications with similarity-preferential mechanism

Author

Listed:
  • Jianlin Zhou

    (Beijing Normal University)

  • An Zeng

    (Beijing Normal University)

  • Ying Fan

    (Beijing Normal University)

  • Zengru Di

    (Beijing Normal University)

Abstract

Along with the advance of internet and fast updating of information, nowadays it is much easier to search and acquire scientific publications. To identify the high quality articles from the paper ocean, many ranking algorithms have been proposed. One of these methods is the famous PageRank algorithm which was originally designed to rank web pages in online systems. In this paper, we introduce a preferential mechanism to the PageRank algorithm when aggregating resource from different nodes to enhance the effect of similar nodes. The validation of the new method is performed on the data of American Physical Society journals. The results indicate that the similarity-preferential mechanism improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Though our method is only applied to citation networks in this paper, it can be naturally used in many other real systems, such as designing search engines in the World Wide Web and revealing the leaderships in social networks.

Suggested Citation

  • Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2016. "Ranking scientific publications with similarity-preferential mechanism," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 805-816, February.
    • Handle: RePEc:spr:scient:v:106:y:2016:i:2:d:10.1007_s11192-015-1805-1
    DOI: 10.1007/s11192-015-1805-1
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    References listed on IDEAS

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    2. 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.
    3. 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).
    4. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    5. Tortosa, Leandro & Vicent, Jose F. & Yeghikyan, Gevorg, 2021. "An algorithm for ranking the nodes of multiplex networks with data based on the PageRank concept," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    6. 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.
    7. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
    8. Niu, Qikai & Zhou, Jianlin & Zeng, An & Fan, Ying & Di, Zengru, 2016. "Which publication is your representative work?," Journal of Informetrics, Elsevier, vol. 10(3), pages 842-853.
    9. Yongjun Zhu & Erjia Yan & Min Song, 2016. "Understanding the evolving academic landscape of library and information science through faculty hiring data," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1461-1478, September.
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    12. 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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