, Volume 79, Issue 3, pp 681–702 | Cite as

Using co-outlinks to mine heterogeneous networks



Clustering is applied to web co-outlink analysis to represent the heterogeneous nature of the World Wide Web in terms of the “triple helix” model (university-industry-government). An initial categorization is based on families of websites, which is then matched with Spanish institutions from diverse sectors represented on the Web, to uncover cognitive structures and related subgroups with common interests and confirm the junction of sectors of the “triple helix” model. We may conclude that the clustering method applied to web co-outlink analysis works when fully institutionalized organizations are studied, to make their interconnections manifest.


Mass Medium Heterogeneous Network Triple Helix Business Association Public Health Center 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Adamic, L. (2005), The Small World Web. Available: [2006, October 12th].
  2. Aguillo, I. F. (2002), Herramientas avanzadas para la búsqueda de información médica en el web. Atención Primaria, 29 (4): 246–253.Google Scholar
  3. Aguillo, I. F., Granadino, B. (2006), Indicadores web para medir la presencia de las universidades en la Red. In: Roca, Genís (coord.), La presencia de las universidades en la Red [online monograph]. Revista de Universidad y Sociedad del Conocimiento (RUSC), 3 (1), Available: [2007, February 20th].
  4. Ajiferuke, I., Wolfram, D. (2004), Modelling the characteristics of Web page outlinks. Scientometrics, 59 (1): 43–62.CrossRefGoogle Scholar
  5. Bichteler J., Eaton E. A.III (1980), The combined use of bibliographic coupling and cocitation for document retrieval. Journal of the American Society for Information Science, 31 (7): 278–282.CrossRefGoogle Scholar
  6. Björneborn, L., Ingwersen, P. (2001), Perspectives of webometrics. Scientometrics, 50 (1): 65–82.CrossRefGoogle Scholar
  7. Björneborn, L. (2004), Small-World Link Structures across an Academic Web Space: A Library and Information Science Approach (PhD), Univ. of Denmark, 2004.Google Scholar
  8. Björneborn, L., Ingwersen, P. (2004), Toward a basic framework for webometrics. Journal of the American Society for Information Science, 55 (14): 1216–1227.CrossRefGoogle Scholar
  9. Börner, K., Chen, C., Boyack, K. W. (2003), Visualizing knowledge domains. Annual Review of Information Science & Technology, 37: 179–255.CrossRefGoogle Scholar
  10. Boudourides, M. A., Sigrist, B., Alevizos, P. D. (1999), Webometrics and the self-organization of the European Information Society. Available: [2006, September 4th].
  11. Castells, M. (2001), La era de la información: Economía, Sociedad y Cultura. 2a ed. Reimp. Madrid: Alianza. V. 1: La sociedad red.Google Scholar
  12. Faba-Pérez, C., Guerrero-Bote, V. P., Moya-Anegón, F.De. (2004), Methods for analysing web citations: a study of web-coupling in a closed environment. Libri, 54: 43–53.CrossRefGoogle Scholar
  13. García-Santiago, L. (2001), Topología de la Información en la World Wide Web: Modelo Experimental y Bibliométrico en una Red Hipertextual Nacional. PhD. Thesis. University of Granada.Google Scholar
  14. García-Santiago, L. (2003), Extraer y Visualizar Información en Internet: El Web Mining. Gijón, Spain: Trea.Google Scholar
  15. García-Santiago, L. (2006), Visualización y estudio evolutivo de las relaciones de los centros de investigación sobre el cáncer en la World Wide Web. Proceedings of I International Conference on Multidisciplinary Information Sciences and Technologies, InSciT. Mérida, Spain, 25–28 October, 2:72–76.Google Scholar
  16. Granovetter, M. (1973), The strength of weak ties. American Journal of Sociology, 78 (May): 1360–1380.CrossRefGoogle Scholar
  17. Heimeriks, G., Hörlesberger, M., Van Den Besselaar, P. (2003), Mapping communication and collaboration in heterogeneous research networks. Scientometrics, 58 (2): 391–413.CrossRefGoogle Scholar
  18. Heylighen, F. (1999), Web Connectivity Analysis. PCP Research on Intelligent Webs, Available: [2006, September 4th].
  19. Kessler, M. M. (1963), Bibliographic coupling between scientific papers. American Documentation, (June): 10–25.Google Scholar
  20. Kessler, M. M. (1965), Comparison of the results of bibliographic coupling and analytic subject indexing. American Documentation, 16 (3):223–233.CrossRefGoogle Scholar
  21. Kosala, R., Blockeel, H. (2000), Web mining research: a survey. ACM SIGKDD Explorations Newsletter, 2 (1): 1–15.CrossRefGoogle Scholar
  22. Leydesdorff, L., Curran, M. (2000), Mapping university-industry-government relations on the internet: The construction of indicators for a knowledge-based economy. Cybermetrics, 4 (2): 1–17.Google Scholar
  23. Leydesdorff, L., Etzkowitz, H. (1998), The triple helix as a model for innovation studies. Science & Public Policy, 25 (3): 195–203.Google Scholar
  24. Leydesdorff, L., Meyer, M. (2003), The triple helix of university-industry-government relations. Scientometrics, 58 (2): 191–203.CrossRefGoogle Scholar
  25. Leydesdorff, L., Meyer, M. (2005), A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using triple helix indicator. Scientometrics, 65 (1): 3–27.CrossRefGoogle Scholar
  26. Leydesdorff, L., Meyer, M. (2007), The scientometrics of a triple helix of university-industry-government relations: Introduction to the topical issue. Scientometrics, 70 (2): 207–222. Available: [2007, August 20th].CrossRefGoogle Scholar
  27. Leydesdorff, L., Vaughan, L. (2006), Co-occurrence matrices and their applications in information science: Extending ACA to the web environment. Journal of the American Society for Information Science, 57 (12): 1616–1628.CrossRefGoogle Scholar
  28. O’Neill, E. T. (1998), Characteristics of web accessible information. IFLA Journal, 24 (2): 114–116.CrossRefGoogle Scholar
  29. Ortega, J. L., Aguillo I., Prieto, J. A. (2006), Longitudinal study of content and elements in the scientific web environment. Journal of Information Science, 32 (4): 344–351.CrossRefGoogle Scholar
  30. Pennock, D. M., Flake, G.W., Lawrence, S., Glover, E., LEE GILES, C. (2002) Winners don’t take all: Characterizing the competition for links on the web. Proceedings of the National Academy of Sciences, 99 (8): 5207–5211.MATHCrossRefGoogle Scholar
  31. Persson, O. (1994), The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for Information Science, 45 (1): 31–38.CrossRefGoogle Scholar
  32. Prime, C., Bassecoulard, E., Zitt, M. (2002), Co-citations and co-sitations: a cautionary view on an analogy. Scientometrics, 54 (2): 291–308.CrossRefGoogle Scholar
  33. Rousseau, R., (1997), Sitations: an exploratory study. Cybermetrics, 1 (1), Available: [2006, November 10th].
  34. Smith, A. G. (2004), Web links as analogues of citations. Information Research, 9 (4), paper 188. Available: [2006, September 26th].
  35. Thelwall, M. (2002A), Conceptualizing documentation on the web: An evaluation of different heuristic-based models for counting links between university web sites. Journal of the American Society for Information Science, 53 (12): 995–1005.CrossRefGoogle Scholar
  36. Thelwall, M. (2002B), Web use and peer interconnectivity metrics for academic web sites. Journal of Information Science, 27 (6): 393–401.CrossRefGoogle Scholar
  37. Thelwall, M., Aguillo, I. F. (2003), La salud de las web universitarias españolas. Revista Española de Documentación Científica. 26 (3): 291–305.Google Scholar
  38. Thelwall, M., Vaughan, L., Cothey, V., Li, X., Smith, A. G. (2003), Which academic subjects have most online impact? A pilot study and a new classification process. Online Information Review, 27 (5): 333–343.CrossRefGoogle Scholar
  39. Thelwall, M., Vaughan, L., Björneborn, L., (2005), Webometrics. In: Cronin, B. (Ed.), Annual Review of Information Science and Technology. Information Today Inc., Medford, NJ, 39: 81–135.Google Scholar
  40. Tremayne, M. (2004), The web of context: applying network theory to the use of hyperlinks in journalism on the web. Journalism and Mass Communication Quarterly, Summer, 81 (2): 237–253.Google Scholar
  41. Trochim, W. M. K. (1996), Evaluation Websites. Available: [1999, January 11].
  42. Uberti T. E., Maggioni M. A., (2004), Infrastrutture ICT e relazionalità potenziale. Un esercizio di “hyperlinks counting” a livello sub-nazionale. Quaderno DISEIS, 0402, Università Cattolica del sacro Cuore, Milano, Vita e Pensiero.Google Scholar
  43. Vaughan, L. (2004), Web hyperlinks reflect business performance: a study of U.S. and Chinese IT companies. Canadian Journal of Information and Library Science, 28 (1): 17–31.Google Scholar
  44. Vaughan, L., Shaw, D. (2003), Bibliographic and web citations: What is the difference?. Journal of the American Society for Information Science and Technology, 54 (14): 1313–1322.CrossRefGoogle Scholar
  45. Vaughan, L., Thelwall, M. (2005), A modelling approach to uncover hyperlink patterns: the case of Canadian universities. Information Processing & Management, 41: 347–359.CrossRefGoogle Scholar
  46. Vaughan, L., Wu, G. (2004), Links to commercial websites as a source of business information. Scientometrics, 60 (3): 487–496.CrossRefGoogle Scholar
  47. Vladutz, G., Cook, J. (1984), Bibliographic coupling and subject relatedness: challenges to an information society. Proceedings of the 47th American Society for Information Science, 204–207.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Lola García-Santiago
    • 1
    • 2
  • Felix De Moya-Anegón
    • 1
    • 2
  1. 1.CSICUnidad Asociada Grupo SCImagoMadridSpain
  2. 2.Department of Library and Information ScienceUniversity of GranadaGranadaSpain

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