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  • × author_ss:"Wang, Y."
  • × author_ss:"Liu, X."
  1. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.00 
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    Abstract
    An innovative model to measure the influence among scientific journals is developed in this study. This model is based on the path analysis of a journal citation network, and its output is a journal influence matrix that describes the directed influence among all journals. Based on this model, an index of journals' overall influence, the quality-structure index (QSI), is derived. Journal ranking based on QSI has the advantage of accounting for both intrinsic journal quality and the structural position of a journal in a citation network. The QSI also integrates the characteristics of two prevailing streams of journal-assessment measures: those based on bibliometric statistics to approximate intrinsic journal quality, such as the Journal Impact Factor, and those using a journal's structural position based on the PageRank-type of algorithm, such as the Eigenfactor score. Empirical results support our finding that the new index is significantly closer to scholars' subjective perception of journal influence than are the two aforementioned measures. In addition, the journal influence matrix offers a new way to measure two-way influences between any two academic journals, hence establishing a theoretical basis for future scientometrics studies to investigate the knowledge flow within and across research disciplines.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.643-653
  2. Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023) 0.00 
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    Abstract
    This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127

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