Search (4 results, page 1 of 1)

  • × author_ss:"Li, D."
  1. 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) 0.02
    0.016780771 = product of:
      0.050342314 = sum of:
        0.050342314 = product of:
          0.10068463 = sum of:
            0.10068463 = weight(_text_:networks in 4759) [ClassicSimilarity], result of:
              0.10068463 = score(doc=4759,freq=6.0), product of:
                0.22247115 = queryWeight, product of:
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.047034867 = queryNorm
                0.45257387 = fieldWeight in 4759, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4759)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
  2. Shen, J.; Yao, L.; Li, Y.; Clarke, M.; Wang, L.; Li, D.: Visualizing the history of evidence-based medicine : a bibliometric analysis (2013) 0.01
    0.009688382 = product of:
      0.029065145 = sum of:
        0.029065145 = product of:
          0.05813029 = sum of:
            0.05813029 = weight(_text_:networks in 1090) [ClassicSimilarity], result of:
              0.05813029 = score(doc=1090,freq=2.0), product of:
                0.22247115 = queryWeight, product of:
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.047034867 = queryNorm
                0.26129362 = fieldWeight in 1090, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1090)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    The aim of this paper is to visualize the history of evidence-based medicine (EBM) and to examine the characteristics of EBM development in China and the West. We searched the Web of Science and the Chinese National Knowledge Infrastructure database for papers related to EBM. We applied information visualization techniques, citation analysis, cocitation analysis, cocitation cluster analysis, and network analysis to construct historiographies, themes networks, and chronological theme maps regarding EBM in China and the West. EBM appeared to develop in 4 stages: incubation (1972-1992 in the West vs. 1982-1999 in China), initiation (1992-1993 vs. 1999-2000), rapid development (1993-2000 vs. 2000-2004), and stable distribution (2000 onwards vs. 2004 onwards). Although there was a lag in EBM initiation in China compared with the West, the pace of development appeared similar. Our study shows that important differences exist in research themes, domain structures, and development depth, and in the speed of adoption between China and the West. In the West, efforts in EBM have shifted from education to practice, and from the quality of evidence to its translation. In China, there was a similar shift from education to practice, and from production of evidence to its translation. In addition, this concept has diffused to other healthcare areas, leading to the development of evidence-based traditional Chinese medicine, evidence-based nursing, and evidence-based policy making.
  3. Shen, X.; Li, D.; Shen, C.: Evaluating China's university library Web sites using correspondence analysis (2006) 0.01
    0.008496767 = product of:
      0.0254903 = sum of:
        0.0254903 = product of:
          0.0509806 = sum of:
            0.0509806 = weight(_text_:22 in 5277) [ClassicSimilarity], result of:
              0.0509806 = score(doc=5277,freq=2.0), product of:
                0.1647081 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.047034867 = queryNorm
                0.30952093 = fieldWeight in 5277, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=5277)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    22. 7.2006 16:40:18
  4. Li, D.: Knowledge representation and discovery based on linguistic atoms (1998) 0.01
    0.0063725756 = product of:
      0.019117726 = sum of:
        0.019117726 = product of:
          0.038235452 = sum of:
            0.038235452 = weight(_text_:22 in 3836) [ClassicSimilarity], result of:
              0.038235452 = score(doc=3836,freq=2.0), product of:
                0.1647081 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.047034867 = queryNorm
                0.23214069 = fieldWeight in 3836, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3836)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Footnote
    Contribution to a special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997