Search (10 results, page 1 of 1)

0.013301588 = product of:
  0.1064127 = sum of:
    0.1064127 = weight(_text_:property in 1341) [ClassicSimilarity], result of:
      0.1064127 = score(doc=1341,freq=2.0), product of:
        0.25336683 = queryWeight, product of:
          6.335595 = idf(docFreq=212, maxDocs=44218)
          0.039991006 = queryNorm
        0.4199946 = fieldWeight in 1341, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          6.335595 = idf(docFreq=212, maxDocs=44218)
          0.046875 = fieldNorm(doc=1341)
  0.125 = coord(1/8)

Abstract

The debate about which similarity measure one should use for the normalization in the case of Author Co-citation Analysis (ACA) is further complicated when one distinguishes between the symmetrical co-citation - or, more generally, co-occurrence - matrix and the underlying asymmetrical citation - occurrence - matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, one should use the Jaccard index, but preferentially after adding the number of total citations (i.e., occurrences) on the main diagonal. Unlike Salton's cosine and the Pearson correlation, the Jaccard index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the two sets. Since the correlations in the co-occurrence matrix may be spurious, this property of the Jaccard index can be considered as an advantage in this case.

0.012246466 = product of:
  0.09797173 = sum of:
    0.09797173 = weight(_text_:network in 6524) [ClassicSimilarity], result of:
      0.09797173 = score(doc=6524,freq=10.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.5501096 = fieldWeight in 6524, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6524)
  0.125 = coord(1/8)

Abstract

Fields of technoscience like biotechnology develop in a network mode: disciplinary insights from different backgrounds are recombined as competing innovation systems are continuously reshaped. The ongoing process of integration at the European level generates an additional network of transnational collaborations. Using the title words of scientific publications in five core journals of biotechnology, multivariate analysis is used to distinguish between the intellectual organization of the publications in terms of title words and the institutional network in terms of addresses of documents. The interaction among the representation of intellectual space in terms of words and co-words, and the potentially European network system is compared with the document sets with American and Japanese addresses. The European system can also be decomposed in terms of the contributions of member states. Whereas a European vocabulary can be made visible at the global level, this communality disappears by this decomposition. The network effect at the European level can be considered as institutional more than cognitive

0.009294414 = product of:
  0.07435531 = sum of:
    0.07435531 = weight(_text_:network in 4427) [ClassicSimilarity], result of:
      0.07435531 = score(doc=4427,freq=4.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.41750383 = fieldWeight in 4427, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.046875 = fieldNorm(doc=4427)
  0.125 = coord(1/8)

Abstract

The aggregated journal-journal citation matrix derived from Journal Citation Reports 2001 can be decomposed into a unique subject classification using the graph-analytical algorithm of bi-connected components. This technique was recently incorporated in software tools for social network analysis. The matrix can be assessed in terms of its decomposability using articulation points which indicate overlap between the components. The articulation points of this set did not exhibit a next-order network of "general science" journals. However, the clusters differ in size and in terms of the internal density of their relations. A full classification of the journals is provided in the Appendix. The clusters can also be extracted and mapped for the visualization.

0.006572143 = product of:
  0.052577145 = sum of:
    0.052577145 = weight(_text_:network in 453) [ClassicSimilarity], result of:
      0.052577145 = score(doc=453,freq=2.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.29521978 = fieldWeight in 453, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.046875 = fieldNorm(doc=453)
  0.125 = coord(1/8)

Abstract

In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004 (Thomson ISI, Philadelphia, PA), and then also in relation to local citation environments that can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization; otherwise, the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator's local character. Maps showing interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments, which is available online.

0.006572143 = product of:
  0.052577145 = sum of:
    0.052577145 = weight(_text_:network in 2929) [ClassicSimilarity], result of:
      0.052577145 = score(doc=2929,freq=2.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.29521978 = fieldWeight in 2929, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.046875 = fieldNorm(doc=2929)
  0.125 = coord(1/8)

Abstract

The launching of Scopus and Google Scholar, and methodological developments in social-network analysis have made many more indicators for evaluating journals available than the traditional impact factor, cited half-life, and immediacy index of the ISI. In this study, these new indicators are compared with one another and with the older ones. Do the various indicators measure new dimensions of the citation networks, or are they highly correlated among themselves? Are they robust and relatively stable over time? Two main dimensions are distinguished - size and impact - which together shape influence. The h-index combines the two dimensions and can also be considered as an indicator of reach (like Indegree). PageRank is mainly an indicator of size, but has important interactions with centrality measures. The Scimago Journal Ranking (SJR) indicator provides an alternative to the journal impact factor, but the computation is less easy.

0.0054767863 = product of:
  0.04381429 = sum of:
    0.04381429 = weight(_text_:network in 6113) [ClassicSimilarity], result of:
      0.04381429 = score(doc=6113,freq=2.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.2460165 = fieldWeight in 6113, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.0390625 = fieldNorm(doc=6113)
  0.125 = coord(1/8)

Abstract

Co-occurrence matrices, such as cocitation, coword, and colink matrices, have been used widely in the information sciences. However, confusion and controversy have hindered the proper statistical analysis of these data. The underlying problem, in our opinion, involved understanding the nature of various types of matrices. This article discusses the difference between a symmetrical cocitation matrix and an asymmetrical citation matrix as well as the appropriate statistical techniques that can be applied to each of these matrices, respectively. Similarity measures (such as the Pearson correlation coefficient or the cosine) should not be applied to the symmetrical cocitation matrix but can be applied to the asymmetrical citation matrix to derive the proximity matrix. The argument is illustrated with examples. The study then extends the application of co-occurrence matrices to the Web environment, in which the nature of the available data and thus data collection methods are different from those of traditional databases such as the Science Citation Index. A set of data collected with the Google Scholar search engine is analyzed by using both the traditional methods of multivariate analysis and the new visualization software Pajek, which is based on social network analysis and graph theory.

0.0054767863 = product of:
  0.04381429 = sum of:
    0.04381429 = weight(_text_:network in 2373) [ClassicSimilarity], result of:
      0.04381429 = score(doc=2373,freq=2.0), product of:
        0.17809492 = queryWeight, product of:
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.039991006 = queryNorm
        0.2460165 = fieldWeight in 2373, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4533744 = idf(docFreq=1398, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2373)
  0.125 = coord(1/8)

Abstract

With the program HistCite(TM) it is possible to generate and visualize the most relevant papers in a set of documents retrieved from the Science Citation Index. Historical reconstructions of scientific developments can be represented chronologically as developments in networks of citation relations extracted from scientific literature. This study aims to go beyond the historical reconstruction of scientific knowledge, enriching the output of HistCiteTM with algorithms from social-network analysis and information theory. Using main-path analysis, it is possible to highlight the structural backbone in the development of a scientific field. The expected information value of the message can be used to indicate whether change in the distribution (of citations) has occurred to such an extent that a path-dependency is generated. This provides us with a measure of evolutionary change between subsequent documents. The forgetting and rewriting of historically prior events at the research front can thus be indicated. These three methods - HistCite, main path and path dependent transitions - are applied to a set of documents related to fullerenes and the fullerene-like structures known as nanotubes.

0.002031836 = product of:
  0.016254688 = sum of:
    0.016254688 = product of:
      0.032509375 = sum of:
        0.032509375 = weight(_text_:22 in 1621) [ClassicSimilarity], result of:
          0.032509375 = score(doc=1621,freq=2.0), product of:
            0.1400417 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039991006 = queryNorm
            0.23214069 = fieldWeight in 1621, 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=1621)
      0.5 = coord(1/2)
  0.125 = coord(1/8)

Date

22. 5.2003 19:48:04

0.002031836 = product of:
  0.016254688 = sum of:
    0.016254688 = product of:
      0.032509375 = sum of:
        0.032509375 = weight(_text_:22 in 4460) [ClassicSimilarity], result of:
          0.032509375 = score(doc=4460,freq=2.0), product of:
            0.1400417 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039991006 = queryNorm
            0.23214069 = fieldWeight in 4460, 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=4460)
      0.5 = coord(1/2)
  0.125 = coord(1/8)

Date

6.11.2005 19:02:22

0.002031836 = product of:
  0.016254688 = sum of:
    0.016254688 = product of:
      0.032509375 = sum of:
        0.032509375 = weight(_text_:22 in 2761) [ClassicSimilarity], result of:
          0.032509375 = score(doc=2761,freq=2.0), product of:
            0.1400417 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.039991006 = queryNorm
            0.23214069 = fieldWeight in 2761, 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=2761)
      0.5 = coord(1/2)
  0.125 = coord(1/8)

Date

22. 3.2009 19:07:20