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Topic‐based PageRank on author cocitation networks

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  • Ying Ding

Abstract

Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h‐index, PageRank, and weighted PageRank), but most of them are topic‐independent. This paper proposes topic‐dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author‐conference‐topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.

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  • Ying Ding, 2011. "Topic‐based PageRank on author cocitation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(3), pages 449-466, March.
    • Handle: RePEc:bla:jamist:v:62:y:2011:i:3:p:449-466
    DOI: 10.1002/asi.21467
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    Cited by:

    1. Xinhai Lu & Yanwei Zhang & Chaoran Lin & Feng Wu, 2021. "Evolutionary Overview and Prediction of Themes in the Field of Land Degradation," Land, MDPI, vol. 10(3), pages 1-23, March.
    2. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.

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