How Web 1.0 fails: the mismatch between hyperlinks and clickstreams

  • Lingfei Wu
  • Robert Ackland
Original Article


The core of the Web is a hyperlink navigation system collaboratively set up by webmasters to help users find desired information. While it is well known that search engines are important for navigation, the extent to which search has led to a mismatch between hyperlinks and the pathways that users actually take has not been quantified. By applying network science to publicly available hyperlink and clickstream data for approximately 1,000 of the top Web sites, we show that the mismatch between hyperlinks and clickstreams is indeed substantial. We demonstrate that this mismatch has arisen because webmasters attempt to build a global virtual world without geographical or cultural boundaries, but users in fact prefer to navigate within more fragmented, language-based groups of Web sites. We call this type of behavior “preferential navigation” and find that it is driven by “local” search engines.


Clickstream Hyperlink Search engine Navigation Social network analysis 



We thank Jonathan J. H. Zhu, Lexing Xie, Paul Thomas, Hai Liang, and the reviewers for providing comments on an earlier version of this paper.


  1. Ackland R (2010) WWW hyperlink networks. In: Hansen DL, Shneiderman B, Smith MA (eds) Analyzing social media networks with NodeXL: insights from a connected world. Morgan-Kaufmann, BurlingtonGoogle Scholar
  2. Ackland R, O’Neil M (2011) Online collective identity: the case of the environmental movement. Soc Netw 33:177–190CrossRefGoogle Scholar
  3. Barabási A, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509CrossRefMathSciNetGoogle Scholar
  4. Barnett GA, Chon BS, Rosen D (2001) The structure of internet flows in cyberspace. Netw Commun Stud (NETCOM) 15(1–2):61–80Google Scholar
  5. Barnett GA, Park HW (2005) The structure of international internet hyperlinks and bilateral bandwidth. Annales des Telecommunications 60(9–10):1115–1132Google Scholar
  6. Barnett GA, Sung EJ (2005) Culture and the structure of the international hyperlink network. J Comput Med Commun 11(1):217–238CrossRefGoogle Scholar
  7. Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, Chute R, Rodriguez M, Balakireva L (2009) Clickstream data yields high-resolution maps of science. PLoS One 4(3):e4803CrossRefGoogle Scholar
  8. Brainerd J, Becker B (2001) Case study: e-commerce clickstream visualization. In: Proceedings of the IEEE symposium on information visualization 2001 (INFOVIS’01), p 153. IEEE Computer SocietyGoogle Scholar
  9. Catledge LD, Pitkow JE (1995) Characterizing browsing strategies in the World Wide Web. Comput Netw ISDN Syst 27:1065–1073CrossRefGoogle Scholar
  10. Cattuto C, Loreto V, Pietronero L (2007) Semiotic dynamics and collaborative tagging. Proc Natl Acad Sci 104(5):1461CrossRefGoogle Scholar
  11. Freeman L (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239CrossRefGoogle Scholar
  12. Fruchterman T, Reingold E (1991) Graph drawing by force-directed placement. Softw Pract Exp 21(11):1129–1164CrossRefGoogle Scholar
  13. Garlaschelli D, Caldarelli G, Pietronero L (2003) Universal scaling relations in food webs. Nature 423(6936):165–168CrossRefGoogle Scholar
  14. Hindman M, Tsioutsiouliklis K, Johnson J (2003) Googlearchy: how a few heavily-linked sites dominate politics on the web. In: Annual meeting of the midwest political science association, vol 4. Citeseer, pp 1–33Google Scholar
  15. Kim D, Im I, Atluri V (2005) A clickstream-based collaborative filtering recommendation model for e-commerce. In: Seventh IEEE international conference on e-commerce technology, 2005 (CEC 2005). IEEE, pp 84–91Google Scholar
  16. Kim DH, Atluri V, Bieber M, Adam N, Yesha Y (2004) A clickstream-based collaborative filtering personalization model: towards a better performance. In: Proceedings of the 6th annual ACM international workshop on web information and data management. Association for Computing MachineryGoogle Scholar
  17. Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632CrossRefMathSciNetzbMATHGoogle Scholar
  18. Meiss MR., Gonalves B, Ramasco JJ, Flammini A, Menczer F (2010) Agents, bookmarks and clicks: a topical model of web navigation. In: Proceedings of the 21st ACM conference on Hypertext and hypermediaGoogle Scholar
  19. Page L, Brin S, Motwani R., Winograd T (1997) Pagerank: bringing order to the web. Accessed 29 Jan 2001
  20. Park HW, Barnett GA, Chung CJ (2011) Structural changes in the 2003–2009 global hyperlink network. Glob Netw 11(4):522–542CrossRefGoogle Scholar
  21. Park HW, Kim CS, Barnett GA (2004) Socio-communicational structure among political actors on the web in South Korea: the dynamics of digital presence in cyberspace. New Media Soc 6(3):403–423CrossRefGoogle Scholar
  22. Qiu F, Liu Z, Cho J (2005) Analysis of user web traffic with a focus on search activities. In: Proceedings of the international workshop on the web and databasesGoogle Scholar
  23. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106CrossRefGoogle Scholar
  24. Ritzer G, Jurgenson N (2010) Production, consumption, prosumption. J Consum Cult 10(1):13CrossRefGoogle Scholar
  25. Schneider F, Feldmann A, Krishnamurthy B, Willinger W (2009) Understanding online social network usage from a network perspective. In: Proceedings of the 9th ACM SIGCOMM conference on internet measurement conference. ACM, pp 35–48Google Scholar
  26. Shadbolt N, Hall W, Berners-Lee T (2006) The semantic web revisited. Intell Syst IEEE 21(3):96–101CrossRefGoogle Scholar
  27. Shumate M, Dewitt L (2008) The North/South divide in NGO hyperlink networks. J Comput Med Commu 13:405–428CrossRefGoogle Scholar
  28. Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440CrossRefGoogle Scholar
  29. Wu F, Huberman B (2007) Novelty and collective attention. Proc Natl Acad Sci 104(45):17599CrossRefGoogle Scholar
  30. Yamakami T (2006) Regularity analysis using time slot counting in the mobile clickstream. In: Database and expert systems applications, 2006. DEXA’06. 17th international workshop on, IEEE, pp 55–59Google Scholar
  31. Zhuge H (2009) Communities and emerging semantics in semantic link network: discovery and learning. Knowl Data Eng IEEE Trans 21(6):785–799CrossRefMathSciNetGoogle Scholar
  32. Zhuge H (2011) Semantic linking through spaces for cyber-physical-socio intelligence: a methodology. Artif Intell 175(5–6):988–1019Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.School of Human Evolution and Social Change and Center for the Study of Institutional DiversityArizona State UniversityTempeUSA
  2. 2.Australian Demographic and Social Research InstituteAustralian National UniversityCanberraAustralia

Personalised recommendations