World Class Universities on the Web : A network graph view of

Ortega, José Luis and Aguillo, Isidro F. World Class Universities on the Web : A network graph view of, 2008 . In 10th International Conference on Science and Technology Indicators, Vienna (Austria), 17th–20th September 2008. (Unpublished) [Conference poster]

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English abstract

1 Introduction World University Ranking on the Web ( is one of the most important ranking about higher education performance, and, concretely, the principal according to the web dimension of these academic institutions. Its principal wisdom is to cover a large amount of universities which are not present in similar educational rankings. This property allows us to develop a visual picture of the outstanding universities around the World, studying their relationships and their position in a structural dimension. 2 Objectives The purpose of this communication is to present a visual display of the 1,000 most important universities in the World according to the ranking of This map intends to show the topological characteristics of this kind of networks and to describe how the relationships among universities of different countries and continents are. 3 Methods We have selected the first 1,000 high education institutions from the World University Ranking on the Web. A link matrix among this set of universities was built, extracting the data from Yahoo! Search in February 2008. The following queries were used to obtain the links from the university domain (A) to the university domain (B): site:{university domain (A)} linkdomain:{university domain (B)} and the total number of pages indexed in the university domain (A): site: {university domain (A)} Several variables have been used in order to add information about the network configuration. Nodes size shows the volume of web pages that each university makes available on the Web. Colours represent the nationality of each high education organization. Arc size shows the frequency of links between two university domains. We have used Pajek 1.02 to visualise the network. We have selected a cut-off of more than 50 links to improve the network visualization. We have also used the Fruchterman-Reingold algorithm to lay out the network because is the fastest energizing large networks [de NOOY, MRVAR and BATAGELJ, 2005]. 4 Results Figure 1 shows the obtained graph from the 1,000 high education institutions. Firstly, we can appreciate that each university is linked with the universities of its own country. Thus, we can visually detect homogeneous national groups such as Germany (red), UK (green light) or Japan (orange) [ORTEGA, AGUILLO, COTHEY and SCHARHORST, 2008]. However, we can also see that there is countries that do not constitute a compact group such as France (dark blue), Canada (white) and other countries with a small set of universities as Netherlands (dark red). The graph also shows linguistic [THELWALL, TANG and PRICE, 2003] and geographical relationships [THELWALL, 2002]. The European countries are located in the right side of the picture, while the left side is mainly taken up by Asian and American ones. We can also observe that the size is related with the link attraction, because the large universities are located in the core of the network. Nevertheless, we detect countries, concretely Asian ones (China, Japan and Taiwan), with large universities that are far from the core. We suppose that may be caused by a low development of English pages by these countries [VAUGHAN and THELWALL, 2004].

Item type: Conference poster
Keywords: WWW, Webometrics, Network Analysis
Subjects: B. Information use and sociology of information > BB. Bibliometric methods
L. Information technology and library technology > LC. Internet, including WWW.
Depositing user: José Luis Ortega Priego
Date deposited: 03 Oct 2008
Last modified: 02 Oct 2014 12:12


NOOY, W. de, MRVAR, A., BATAGELJ, V. (2005), Exploratory Social Network Analysis with Pajek, Cambridge University Press, Cambridge, UK

ORTEGA, J. L., AGUILLO, I. F., COTHEY, V., SCHARNHORST, A. (2008), Maps of the academic web in the European Higher Education Area - an exploration of visual web indicators. Scientometrics. 74(2): 295-308

THELWALL, M. (2002), Evidence for the existence of geographic trends in university web site interlinking. Journal of Documentation, 58(5): 563-574

THELWALL, M., TANG, R., PRICE, L. (2003), Linguistic Patterns of Academic Web Use in Western Europe. Scientometrics, 56(3): 417-432.

VAUGHAN, L., THELWALL, M. (2004), Search engine coverage bias: evidence and possible causes, Information Processing and Management: an International Journal, 40(4):693-707.


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