Search (12 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.05
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    Date
    22. 1.2011 13:02:21
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245
    Year
    2011
  2. Yan, E.; Ding, Y.: Discovering author impact : a PageRank perspective (2011) 0.02
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    Source
    Information processing and management. 47(2011) no.1, S.125-134
    Year
    2011
  3. Ding, Y.: Topic-based PageRank on author cocitation networks (2011) 0.02
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466
    Year
    2011
  4. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.02
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477
    Year
    2011
  5. Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014) 0.02
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    Abstract
    Bioinformatics is a fast-growing field based on the optimal use of "big data" gathered in genomic, proteomics, and functional genomics research. In this paper, we conduct a comprehensive and in-depth bibliometric analysis of the field of bioinformatics by extracting citation data from PubMed Central full-text. Citation data for the period 2000 to 2011, comprising 20,869 papers with 546,245 citations, was used to evaluate the productivity and influence of this emerging field. Four measures were used to identify productivity; most productive authors, most productive countries, most productive organizations, and most popular subject terms. Research impact was analyzed based on the measures of most cited papers, most cited authors, emerging stars, and leading organizations. Results show the overall trends between the periods 2000 to 2003 and 2004 to 2007 were dissimilar, while trends between the periods 2004 to 2007 and 2008 to 2011 were similar. In addition, the field of bioinformatics has undergone a significant shift, co-evolving with other biomedical disciplines.
  6. Sugimoto, C.R.; Li, D.; Russell, T.G.; Finlay, S.C.; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation (2011) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204
    Year
    2011
  7. He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.5, S.831-845
    Year
    2011
  8. Milojevic, S.; Sugimoto, C.R.; Yan, E.; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words (2011) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953
    Year
    2011
  9. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
    Year
    2011
  10. Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015) 0.01
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    Abstract
    This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
  11. Li, D.; Wang, Y.; Madden, A.; Ding, Y.; Sun, G.G.; Zhang, N.; Zhou, E.: Analyzing stock market trends using social media user moods and social influence (2019) 0.01
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    Abstract
    Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.
  12. 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