Search (3 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  • × theme_ss:"Informetrie"
  1. Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018) 0.02 
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    Abstract
    Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.
  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. 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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