Mapping the network of global science: comparing international co-authorships from 1990 to 2000

International Journal of Technology and Globalisation

Volumn 1, Issue 2, 2005, Pages 185-208

  Wagner, Caroline S ^a   Leydesdorff, Loet ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

co authorships; global science; international cooperation in science and technology; networks

Indexed keywords

EID: 84952963714     PISSN: 14765667     EISSN: 17418194     Source Type: Journal    
DOI: 10.1504/IJTG.2005.007050     Document Type: Article

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40. Previous literature discussing this method include Woolgar (1991), Wouters (1999), Mullins et al. (1977), Luukonen et al. (1993) and Schott (1998).
41. The UK is considered here in its component parts because the Science Citation Index is organised in this way. Addresses are provided as England, Scotland, Wales, and Northern Ireland, and each is accordingly handled as a separate unit for the purposes of this analysis.
42. There are a number of different descriptions of the process of globalisation of industry. One useful description, directly relevant to this paper, is contained in the US National Science Board (2000) and Organisation for Economic Cooperation and Development (2001).
43. The CD-Rom version is preferable to the SCI Extended version found on the internet. The Extended version is regularly updated, and therefore, it would be very difficult to repeat this analysis, even from day to day. Assuming that the extended index can change, the dataset could be altered and therefore the results presented here could not be reproduced.
44. We also estimated the effect of this phenomenon on the data shown here: In 2000 the data created showed 121,432 ‘internationally co-authored documents’. Of these, 6,408 had more addresses than authors (approximately 5%): 20,449 authors and 29,987 addresses. Persson et al. (2003) find that, within this set, 1449 are single authored papers with more than one address.
45. The concepts of ‘core’ and ‘periphery’ used here are those outlined originally by Ben-David (1971).
46. The method of counting affects the outcome. Fractional counting attributes the numbers proportionally, so that the number of authors on any given paper reduces the share of each participating country. A second way of counting is to identify the number of links represented among the countries involved, with each bilateral relationship counting as ‘1’. The normalisation in terms of number of links is more common in network analysis. A third way of counting is integer or whole/distinct count that attributes a count of ‘1’ to each occurrence of authorship by a country created by the participation of researchers from that country. We normalise for the size of countries in a next step and did not wish to mix two types of normalisations.
47. Previous analysts have used a number of different approaches depending upon the question they were bringing to the data. Two measures have been used in scientometric analyses to achieve the weighted results (Luukkonen et al., 1992): bilateral similarity measures, and multilateral (pattern) similarity measures (e.g., Pearson correlations). Luukkonen et al. (1993) state “The Jaccard measure underestimates the collaboration of smaller countries with larger ones, but the Salton measure underestimates the collaboration of smaller countries with each other”. In a careful analysis, we find that Jaccard’s measure does not serve us as well as the Salton Index. Jaccard’s Index provides the intersection of the two countries as a percentage of the sum, while the Salton Index provides the intersection as a weighted percentage. But the difference is more than a factor two: whereas the Jaccard Index focuses on strong links in segments of the database the Salton Index organises the relations geometrically so that they can be visualised as structural patterns of relations. Unlike the Pearson correlation, however, the Salton Index is non-parametrical and therefore not sensitive to the number of zeros (Scott, 2000). Thus, we have used the Salton Index as a measure of the networked relatedness of countries. See also Hamers et al. (1989) and Ahlgren et al. (2003).
48. A sub-set of vertices is called a k-core if every vertex from the sub-set is connected to at least k vertices from the same sub-set.
49. The figures in this section are derived using Pajek software. The lines are proportional – thicker lines represent a more intense relationship. A core group is always displayed as white vertices. Peripheral network members and non-participating countries appear as shaded dots.
50. For information about the software and its applications, see http://vlado.fmf.uni-lj.si/pub/networks/pajek/, last visited, October 2003.
51. Networks that are discussed but not included in the paper are available from the corresponding author via e-mail request.
52. Zitt et al. (2000) use ‘observed/expected’ as the normalisation. The results of using this measure are very akin to using the Jaccard measure, but with even more emphasis on outliers (see Michelet, 1988).
53. Bi-connected components can be considered as nodes in a hierarchy.
54. These visualisations are based on using the algorithm found in Fruchterman and Reingold (1991) and Batagelj and Mrvar (2000).
55. These visualisations are based on using the algorithm found in Kamada and Kawai (1989). While in the previous analysis the objective was to distinguish among networks, we now focus on the inner structure of specific networks.