Representing and Quantifying Rank - Change for the Web Graph

  • Akrivi Vlachou
  • Michalis Vazirgiannis
  • Klaus Berberich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4936)

Abstract

One of the grand research and industrial challenges in recent years is efficient web search, inherently involving the issue of page ranking. In this paper we address the issue of representing and quantifying web ranking trends as a measure of web pages. We study the rank position of a web page among different snapshots of the web graph and propose normalized measures of ranking trends that are comparable among web graph snapshots of different sizes. We define the rank changerate (racer) as a measure quantifying the web graph evolution. Thereafter, we examine different ways to aggregate the rank change rates and quantify the trends over a group of web pages. We outline the problem of identifying highly dynamic web pages and discuss possible future work. In our experimental evaluation we study the dynamics of web pages, especially those highly ranked.

Keywords

PageRank Web Graph Web Dynamics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project (1998)Google Scholar
  2. 2.
    Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. Journal of the ACM 46(5), 604–632 (1999)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Amitay, E., Carmel, D., Herscovici, M., Lempel, R., Soffer, A.: Trend detection through temporal link analysis. J. Am. Soc. Inf. Sci. Technol. 55(14), 1270–1281 (2004)CrossRefGoogle Scholar
  4. 4.
    Berberich, K., Vazirgiannis, M., Weikum, G.: T-rank: Time-aware authority ranking. Internet Mathematics 2(3), 309–340 (2004)MathSciNetGoogle Scholar
  5. 5.
    Jeh, G., Widom, J.: Scaling Personalized Web Search. In: Proceedings of the twelfth international conference on World Wide Web, pp. 271–279. ACM Press, New York (2003)CrossRefGoogle Scholar
  6. 6.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: Proceedings of the eleventh international conference on World Wide Web, pp. 517–526. ACM Press, New York (2002)CrossRefGoogle Scholar
  7. 7.
    Nie, Z., Zhang, Y., Wen, J.R., Ma, W.Y.: Object-Level Ranking: Bringing Order to Web Objects. In: WWW, pp. 567–574 (2005) Google Scholar
  8. 8.
    Bianchini, M., Gori, M., Scarselli, F.: Inside pagerank. ACM Trans. Inter. Tech. 5(1), 92–128 (2005)CrossRefGoogle Scholar
  9. 9.
    Borodin, A., Roberts, G.O., Rosenthal, J.S., Tsaparas, P.: Link analysis ranking: algorithms, theory, and experiments. ACM Trans. Inter. Tech. 5(1), 231–297 (2005)CrossRefGoogle Scholar
  10. 10.
    Langville, A.N., Meyer, C.: Deeper Inside PageRank. Internet Mathematics 1(3), 335–380 (2004)MATHMathSciNetGoogle Scholar
  11. 11.
    Pandurangan, G., Raghavan, P., Upfal, E.: Using pagerank to characterize web structure. In: Proc. of Int. Conf. on Computing and Combinatorics, pp. 330–339 (2002)Google Scholar
  12. 12.
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web. Comput. Networks, 309–320 (2000) Google Scholar
  13. 13.
    Dill, S., Kumar, R., McCurley, K.S., Rajagopalan, S., Sivakumar, D., Tomkins, A.: Self-similarity in the web. In: Proc. of Int. Conf. on VLDB, pp. 69–78 (2001) Google Scholar
  14. 14.
    Fetterly, D., Manasse, M., Najork, M., Wiener, J.: A large-scale study of the evolution of web pages. In: Proc. of Int. Conf. on WWW, pp. 669–678 (2003) Google Scholar
  15. 15.
    Ntoulas, A., Cho, J., Olston, C.: What’s New on the Web?: The Evolution of the Web from a Search Engine Perspective. In: Proc. of the 13th Conference on World Wide Web, pp. 1–12. ACM Press, New York (2004)CrossRefGoogle Scholar
  16. 16.
    Cho, J., Roy, S.: Impact of Search Engines on Page Popularity. In: Proc. of the 13th Conf. on World Wide Web, pp. 20–29. ACM Press, New York (2004)CrossRefGoogle Scholar
  17. 17.
    Cho, J., Roy, S., Adams, R.E.: Page Quality: In Search of an Unbiased Web Ranking. In: Proc. of Int. Conf. on Management of Data (SIGMOD), pp. 551–562. ACM Press, New York (2005)CrossRefGoogle Scholar
  18. 18.
    Pandey, S., Roy, S., Olston, C., Cho, J., Chakrabarti, S.: Shuffling a Stacked Deck: The Case for Partially Randomized Ranking of Search Engine Results (2005)Google Scholar
  19. 19.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Proc. of PODS, pp. 102–113 (2001)Google Scholar
  20. 20.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: Proc. of Int. Conf. on WWW, pp. 613–622 (2001) Google Scholar
  21. 21.
    Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. J. ACM 22(4), 469–476 (1975)MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proc. of Int. Conf. ICDE, pp. 421–430 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Akrivi Vlachou
    • 1
  • Michalis Vazirgiannis
    • 1
    • 2
  • Klaus Berberich
    • 3
  1. 1.Department of InformaticsUniv. of Economics and BusinessAthensGreece
  2. 2.Gemo, InriaParisFrance
  3. 3.Max-Planck-Institut für InformatikSaarbrückenGermany

Personalised recommendations