Search (3 results, page 1 of 1)

  • × author_ss:"Ni, C."
  • × author_ss:"Ding, Y."
  1. He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011) 0.00 
    0.0022374375 = product of:
  0.004474875 = sum of:
    0.004474875 = product of:
      0.00894975 = sum of:
        0.00894975 = weight(_text_:a in 4444) [ClassicSimilarity], result of:
          0.00894975 = score(doc=4444,freq=14.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.1685276 = fieldWeight in 4444, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4444)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration.
    Type
    a
  2. Ni, C.; Shaw, D.; Lind, S.M.; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features (2013) 0.00 
    0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 686) [ClassicSimilarity], result of:
          0.008118451 = score(doc=686,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 686, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=686)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.
    Type
    a
  3. 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) 0.00 
    0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 2337) [ClassicSimilarity], result of:
          0.008118451 = score(doc=2337,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 2337, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2337)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Type
    a

Authors

Themes