Search (5 results, page 1 of 1)

  • × author_ss:"Yan, E."
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.01 
    0.012555826 = product of:
  0.025111653 = sum of:
    0.025111653 = product of:
      0.050223306 = sum of:
        0.050223306 = weight(_text_:22 in 1534) [ClassicSimilarity], result of:
          0.050223306 = score(doc=1534,freq=4.0), product of:
            0.18357785 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05242341 = queryNorm
            0.27358043 = fieldWeight in 1534, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1534)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Date
    26.10.2014 20:22:22
  2. Ding, Y.; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other (2012) 0.01 
    0.0052987295 = product of:
  0.010597459 = sum of:
    0.010597459 = product of:
      0.021194918 = sum of:
        0.021194918 = weight(_text_:2 in 274) [ClassicSimilarity], result of:
          0.021194918 = score(doc=274,freq=2.0), product of:
            0.1294644 = queryWeight, product of:
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.05242341 = queryNorm
            0.16371232 = fieldWeight in 274, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.046875 = fieldNorm(doc=274)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.
  3. Yan, E.; Yu, Q.: Using path-based approaches to examine the dynamic structure of discipline-level citation networks (2016) 0.01 
    0.0052987295 = product of:
  0.010597459 = sum of:
    0.010597459 = product of:
      0.021194918 = sum of:
        0.021194918 = weight(_text_:2 in 3053) [ClassicSimilarity], result of:
          0.021194918 = score(doc=3053,freq=2.0), product of:
            0.1294644 = queryWeight, product of:
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.05242341 = queryNorm
            0.16371232 = fieldWeight in 3053, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.046875 = fieldNorm(doc=3053)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    The objective of this paper is to identify the dynamic structure of several time-dependent, discipline-level citation networks through a path-based method. A network data set is prepared that comprises 27 subjects and their citations aggregated from more than 27,000 journals and proceedings indexed in the Scopus database. A maximum spanning tree method is employed to extract paths in the weighted, directed, and cyclic networks. This paper finds that subjects such as Medicine, Biochemistry, Chemistry, Materials Science, Physics, and Social Sciences are the ones with multiple branches in the spanning tree. This paper also finds that most paths connect science, technology, engineering, and mathematics (STEM) fields; 2 critical paths connecting STEM and non-STEM fields are the one from Mathematics to Decision Sciences and the one from Medicine to Social Sciences.
  4. Zhu, Y.; Yan, E.; Song, I.-Y..: ¬The use of a graph-based system to improve bibliographic information retrieval : system design, implementation, and evaluation (2017) 0.01 
    0.0052987295 = product of:
  0.010597459 = sum of:
    0.010597459 = product of:
      0.021194918 = sum of:
        0.021194918 = weight(_text_:2 in 3356) [ClassicSimilarity], result of:
          0.021194918 = score(doc=3356,freq=2.0), product of:
            0.1294644 = queryWeight, product of:
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.05242341 = queryNorm
            0.16371232 = fieldWeight in 3356, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.046875 = fieldNorm(doc=3356)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.480-490
  5. Yan, E.; Li, K.: Which domains do open-access journals do best in? : a 5-year longitudinal study (2018) 0.00 
    0.004415608 = product of:
  0.008831216 = sum of:
    0.008831216 = product of:
      0.017662432 = sum of:
        0.017662432 = weight(_text_:2 in 4257) [ClassicSimilarity], result of:
          0.017662432 = score(doc=4257,freq=2.0), product of:
            0.1294644 = queryWeight, product of:
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.05242341 = queryNorm
            0.13642694 = fieldWeight in 4257, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.4695914 = idf(docFreq=10170, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4257)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Although researchers have begun to investigate the difference in scientific impact between closed-access and open-access journals, studies that focus specifically on dynamic and disciplinary differences remain scarce. This study serves to fill this gap by using a large longitudinal dataset to examine these differences. Using CiteScore as a proxy for journal scientific impact, we employ a series of statistical tests to identify the quartile categories and disciplinary areas in which impact trends differ notably between closed- and open-access journals. We find that closed-access journals have a noticeable advantage in social sciences (for example, business and economics), whereas open-access journals perform well in medical and healthcare domains (for example, health profession and nursing). Moreover, we find that after controlling for a journal's rank and disciplinary differences, there are statistically more closed-access journals in the top 10%, Quartile 1, and Quartile 2 categories as measured by CiteScore; in contrast, more open-access journals in Quartile 4 gained scientific impact from 2011 to 2015. Considering dynamic and disciplinary trends in tandem, we find that more closed-access journals in Social Sciences gained in impact, whereas in biochemistry and medicine, more open-access journals experienced such gains.

Authors

Themes