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  • × author_ss:"Leydesdorff, L."
  1. Leydesdorff, L.; Johnson, M.W.; Ivanova, I.: Toward a calculus of redundancy : signification, codification, and anticipation in cultural evolution (2018) 0.01 
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    Date
    29. 9.2018 11:22:09
  2. Leydesdorff, L.; Heimeriks, G.: ¬The self-organization of the European information society : the case of "biotechnology" (2001) 0.01 
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    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
  3. Leydesdorff, L.: Theories of citation? (1999) 0.01 
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    Abstract
    Citations support the communication of specialist knowledge by allowing authors and readers to make specific selections in several contexts at the same time. In the interactions between the social network of authors and the network of their reflexive communications, a sub textual code of communication with a distributed character has emerged. Citation analysis reflects on citation practices. Reference lists are aggregated in scientometric analysis using one of the available contexts to reduce the complexity: geometrical representations of dynamic operations are reflected in corresponding theories of citation. The specific contexts represented in the modern citation can be deconstructed from the perspective of the cultural evolution of scientific communication
  4. Leydesdorff, L.; Park, H.W.; Wagner, C.: International coauthorship relations in the Social Sciences Citation Index : is internationalization leading the Network? (2014) 0.01 
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    Abstract
    International coauthorship relations have increasingly shaped another dynamic in the natural and life sciences during recent decades. However, much less is known about such internationalization in the social sciences. In this study, we analyze international and domestic coauthorship relations of all citable items in the DVD version of the Social Sciences Citation Index 2011 (SSCI). Network statistics indicate 4 groups of nations: (a) an Asian-Pacific one to which all Anglo-Saxon nations (including the United Kingdom and Ireland) are attributed, (b) a continental European one including also the Latin-American countries, (c) the Scandinavian nations, and (d) a community of African nations. Within the EU-28, 11 of the EU-15 states have dominant positions. In many respects, the network parameters are not so different from the Science Citation Index. In addition to these descriptive statistics, we address the question of the relative weights of the international versus domestic networks. An information-theoretical test is proposed at the level of organizational addresses within each nation; the results are mixed, but the international dimension is more important than the national one in the aggregated sets (as in the Science Citation Index). In some countries (e.g., France), however, the national distribution is leading more than the international one. Decomposition of the United States in terms of states shows a similarly mixed result; more U.S. states are domestically oriented in the SSCI and more internationally in the SCI. The international networks have grown during the last decades in addition to the national ones but not by replacing them.
  5. Leydesdorff, L.; Moya-Anegón, F. de; Nooy, W. de: Aggregated journal-journal citation relations in scopus and web of science matched and compared in terms of networks, maps, and interactive overlays (2016) 0.01 
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    Abstract
    We compare the network of aggregated journal-journal citation relations provided by the Journal Citation Reports (JCR) 2012 of the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) with similar data based on Scopus 2012. First, global and overlay maps were developed for the 2 sets separately. Using fuzzy-string matching and ISSN numbers, we were able to match 10,524 journal names between the 2 sets: 96.4% of the 10,936 journals contained in JCR, or 51.2% of the 20,554 journals covered by Scopus. Network analysis was pursued on the set of journals shared between the 2 databases and the 2 sets of unique journals. Citations among the shared journals are more comprehensively covered in JCR than in Scopus, so the network in JCR is denser and more connected than in Scopus. The ranking of shared journals in terms of indegree (i.e., numbers of citing journals) or total citations is similar in both databases overall (Spearman rank correlation ??>?0.97), but some individual journals rank very differently. Journals that are unique to Scopus seem to be less important-they are citing shared journals rather than being cited by them-but the humanities are covered better in Scopus than in JCR.
  6. Leydesdorff, L.: Clusters and maps of science journals based on bi-connected graphs in Journal Citation Reports (2004) 0.01 
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    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.
  7. Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.01 
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    Abstract
    Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.
  8. Leydesdorff, L.; Rafols, I.: Local emergence and global diffusion of research technologies : an exploration of patterns of network formation (2011) 0.01 
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    Abstract
    Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nanocrystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI subject categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time while the network evolves into an oligopolistic control structure with small-world characteristics. The transition from disciplinary-oriented ("Mode 1") to transfer-oriented ("Mode 2") research is suggested as the crucial difference in explaining the different rates of diffusion between siRNA and NCSC.
  9. Leydesdorff, L.; Nooy, W. de: Can "hot spots" in the sciences be mapped using the dynamics of aggregated journal-journal citation relations (2017) 0.01 
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    Abstract
    Using 3 years of the Journal Citation Reports (2011, 2012, and 2013), indicators of transitions in 2012 (between 2011 and 2013) were studied using methodologies based on entropy statistics. Changes can be indicated at the level of journals using the margin totals of entropy production along the row or column vectors, but also at the level of links among journals by importing the transition matrices into network analysis and visualization programs (and using community-finding algorithms). Seventy-four journals were flagged in terms of discontinuous changes in their citations, but 3,114 journals were involved in "hot" links. Most of these links are embedded in a main component; 78 clusters (containing 172 journals) were flagged as potential "hot spots" emerging at the network level. An additional finding was that PLoS ONE introduced a new communication dynamic into the database. The limitations of the methodology were elaborated using an example. The results of the study indicate where developments in the citation dynamics can be considered as significantly unexpected. This can be used as heuristic information, but what a "hot spot" in terms of the entropy statistics of aggregated citation relations means substantively can be expected to vary from case to case.
  10. Leydesdorff, L.; Shin, J.C.: How to evaluate universities in terms of their relative citation impacts : fractional counting of citations and the normalization of differences among disciplines (2011) 0.01 
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    Abstract
    Fractional counting of citations can improve on ranking of multidisciplinary research units (such as universities) by normalizing the differences among fields of science in terms of differences in citation behavior. Furthermore, normalization in terms of citing papers abolishes the unsolved questions in scientometrics about the delineation of fields of science in terms of journals and normalization when comparing among different (sets of) journals. Using publication and citation data of seven Korean research universities, we demonstrate the advantages and the differences in the rankings, explain the possible statistics, and suggest ways to visualize the differences in (citing) audiences in terms of a network.
  11. Leydesdorff, L.: Betweenness centrality as an indicator of the interdisciplinarity of scientific journals (2007) 0.01 
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    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.
  12. Leydesdorff, L.: How are new citation-based journal indicators adding to the bibliometric toolbox? (2009) 0.01 
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    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.
  13. Leydesdorff, L.; Vaughan, L.: Co-occurrence matrices and their applications in information science : extending ACA to the Web environment (2006) 0.01 
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    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.
  14. Lucio-Arias, D.; Leydesdorff, L.: Main-path analysis and path-dependent transitions in HistCite(TM)-based historiograms (2008) 0.01 
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    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.
  15. Leydesdorff, L.; Moya-Anegón, F.de; Guerrero-Bote, V.P.: Journal maps on the basis of Scopus data : a comparison with the Journal Citation Reports of the ISI (2010) 0.01 
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    Abstract
    Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Because the Scopus database contains a larger number of journals and covers the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is because of (a) the larger number of journals covered by Scopus and (b) the historical record of citations older than 10 years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.
  16. Leydesdorff, L.; Ahrweiler, P.: In search of a network theory of innovations : relations, positions, and perspectives (2014) 0.01 
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  17. Bornmann, L.; Wagner, C.; Leydesdorff, L.: BRICS countries and scientific excellence : a bibliometric analysis of most frequently cited papers (2015) 0.01 
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    Abstract
    The BRICS countries (Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US.
  18. Bauer, J.; Leydesdorff, L.; Bornmann, L.: Highly cited papers in Library and Information Science (LIS) : authors, institutions, and network structures (2016) 0.01 
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  19. Leydesdorff, L.; Nerghes, A.: Co-word maps and topic modeling : a comparison using small and medium-sized corpora (N?<?1.000) (2017) 0.01 
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    Abstract
    Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. Does topic modeling provide an alternative for co-word mapping in research practices using moderately sized document collections? We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n?=?687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the topic models provide sets of words that are very differently organized. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping in small and medium-sized sets; but the paper leaves open the possibility that topic modeling would work well for the semantic mapping of large sets.
  20. Leydesdorff, L.; Bornmann, L.; Mingers, J.: Statistical significance and effect sizes of differences among research universities at the level of nations and worldwide based on the Leiden rankings (2019) 0.01 
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    Abstract
    The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%-the proportion of the top-10% most-highly-cited papers assigned to a university-we pursue a classification using (a) overlapping stability intervals, (b) statistical-significance tests, and (c) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w < .2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities.

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