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Measuring academic influence using heterogeneous author-citation networks

Author

Listed:
  • Fen Zhao

    (University of Windsor)

  • Yi Zhang

    (University of Windsor)

  • Jianguo Lu

    (University of Windsor)

  • Ofer Shai

    (Chan Zuckerberg Initiative Inc.)

Abstract

Academic influence has been traditionally measured by citation counts and metrics derived from it, such as H-index and G-index. PageRank based algorithms have been used to give higher weight to citations from more influential papers. A better metric is to add authors into the citation network so that the importance of authors and papers are evaluated recursively within the same framework. Based on such heterogeneous author-citation academic network, this paper gives a new algorithm for ranking authors. It is tested on two large networks, one in Heath domain that contains about 500 million citation links, the other in Computer Science that contains 8 million links. We find that our method outperforms other 10 methods in terms of the number of award winners identified in their top-k rankings. Surprisingly, our method can identify 8 Turing award winners among top 20 authors. It also demonstrates some interesting phenomenons. For instance, among the top authors, our ranking negatively correlates with citation ranking and paper count ranking.

Suggested Citation

  • Fen Zhao & Yi Zhang & Jianguo Lu & Ofer Shai, 2019. "Measuring academic influence using heterogeneous author-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1119-1140, March.
    • Handle: RePEc:spr:scient:v:118:y:2019:i:3:d:10.1007_s11192-019-03010-5
    DOI: 10.1007/s11192-019-03010-5
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    Cited by:

    1. Xipeng Liu & Xinmiao Li, 2022. "Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
    2. Xie, Qing & Zhang, Xinyuan & Kim, Giyeong & Song, Min, 2022. "Exploring the influence of coauthorship with top scientists on researchers’ affiliation, research topic, productivity, and impact," Journal of Informetrics, Elsevier, vol. 16(3).
    3. 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.
    4. Hayat D. Bedru & Chen Zhang & Feng Xie & Shuo Yu & Iftikhar Hussain, 2023. "CLARA: citation and similarity-based author ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1091-1117, February.
    5. 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).
    6. Yonghe Lu & Jiayi Luo & Ying Xiao & Hou Zhu, 2021. "Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6937-6963, August.
    7. Tehmina Amjad & Yusra Rehmat & Ali Daud & Rabeeh Ayaz Abbasi, 2020. "Scientific impact of an author and role of self-citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 915-932, February.
    8. Zhang, Xinyuan & Xie, Qing & Song, Min, 2021. "Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Yi Zhang & Fen Zhao & Jianguo Lu, 2019. "P2V: large-scale academic paper embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 399-432, October.
    10. Gangan Prathap, 2019. "Expected, observed and relative paper scores from heterogeneous author-paper-citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1275-1279, May.

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