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Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers

Author

Listed:
  • Yongjun Zhang

    (Hohai University
    Huaiyin Institute of Technology)

  • Jialin Ma

    (Huaiyin Institute of Technology)

  • Zijian Wang

    (Hohai University)

  • Bolun Chen

    (Huaiyin Institute of Technology)

  • Yongtao Yu

    (Huaiyin Institute of Technology)

Abstract

With the explosive growth of academic writing, it is difficult for researchers to find significant papers in their area of interest. In this paper, we propose a pipeline model, named collective topical PageRank, to evaluate the topic-dependent impact of scientific papers. First, we fit the model to a correlation topic model based on the textual content of papers to extract scientific topics and correlations. Then, we present a modified PageRank algorithm, which incorporates the venue, the correlations of the scientific topics, and the publication year of each paper into a random walk to evaluate the paper’s topic-dependent academic impact. Our experiments showed that the model can effectively identify significant papers as well as venues for each scientific topic, recommend papers for further reading or citing, explore the evolution of scientific topics, and calculate the venues’ dynamic topic-dependent academic impact.

Suggested Citation

  • Yongjun Zhang & Jialin Ma & Zijian Wang & Bolun Chen & Yongtao Yu, 2018. "Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1345-1372, March.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2626-1
    DOI: 10.1007/s11192-017-2626-1
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    References listed on IDEAS

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    1. Marlie MacLean & Catherine Davies & Grant Lewison & Joe Anderson, 1998. "Evaluating the research activity and impact of funding agencies," Research Evaluation, Oxford University Press, vol. 7(1), pages 7-16, April.
    2. Ying Ding, 2011. "Topic-based PageRank on author cocitation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 449-466, March.
    3. Erjia Yan, 2014. "Topic-based Pagerank: toward a topic-level scientific evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 407-437, August.
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    Cited by:

    1. Yuetong Chen & Hao Wang & Baolong Zhang & Wei Zhang, 2022. "A method of measuring the article discriminative capacity and its distribution," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3317-3341, June.
    2. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.
    3. Xie, Zheng & Lv, Yiqin & Song, Yiping & Wang, Qi, 2024. "Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers," Journal of Informetrics, Elsevier, vol. 18(2).
    4. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.

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