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  • × author_ss:"Ding, Y."
  • × year_i:[2010 TO 2020}
  1. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.02 
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    Abstract
    Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
    Object
    P-Rank
  2. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.01 
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    Date
    22. 1.2011 13:02:21
  3. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.01 
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    Date
    22. 8.2014 16:52:04
  4. Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018) 0.01 
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    Abstract
    Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.

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