Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Algorithms and Models for the Web-Graph

WAW 2012: Algorithms and Models for the Web Graph pp 126–137Cite as

Dynamic PageRank Using Evolving Teleportation

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7323))

Abstract

The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly generalizes PageRank because the importance of a node will converge to the PageRank values if the external influence stops changing. We demonstrate the effectiveness of the evolving teleportation on the Wikipedia graph and the Twitter social network. The external interest is given by the number of hourly visitors to each page and the number of monthly tweets for each user.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abiteboul, S., Preda, M., Cobena, G.: Adaptive on-line page importance computation. In: WWW, pp. 280–290. ACM (2003)

    Google Scholar 

  2. Ahmed, N., Atiya, A., El Gayar, N., El-Shishiny, H.: An empirical comparison of machine learning models for time series forecasting. Econ. Rev. 29(5-6), 594–621 (2010)

    Article  Google Scholar 

  3. Bagrow, J., Wang, D., Barabási, A.: Collective response of human populations to large-scale emergencies. PloS one 6(3), e17680 (2011)

    Google Scholar 

  4. Becchetti, L., Castillo, C., Donato, D., Baeza-Yates, R., Leonardi, S.: Link analysis for web spam detection. ACM Trans. Web 2(1), 1–42 (2008)

    Article  Google Scholar 

  5. Bianchini, M., Gori, M., Scarselli, F.: Inside PageRank. ACM Transactions on Internet Technologies 5(1), 92–128 (2005)

    Article  Google Scholar 

  6. Boldi, P.: TotalRank: Ranking without damping. In: WWW, pp. 898–899 (2005)

    Google Scholar 

  7. Boldi, P., Santini, M., Vigna, S.: Paradoxical effects in PageRank incremental computations. Internet Mathematics 2(2), 387–404 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bonacich, P.: Power and centrality: A family of measures. American Journal of Sociology, 1170–1182 (1987)

    Google Scholar 

  9. Chien, S., Dwork, C., Kumar, R., Simon, D., Sivakumar, D.: Link evolution: Analysis and algorithms. Internet Mathematics 1(3), 277–304 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  10. Constantine, P., Gleich, D.: Random alpha PageRank. Internet Mathematics 6(2), 189–236 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  11. Das Sarma, A., Gollapudi, S., Panigrahy, R.: Estimating PageRank on graph streams. In: SIGMOD, pp. 69–78. ACM (2008)

    Google Scholar 

  12. Dunlavy, D.M., Kolda, T.G., Acar, E.: Temporal link prediction using matrix and tensor factorizations. TKDD 5(2), 10:1–10:27 (2011)

    Google Scholar 

  13. Embree, M., Lehoucq, R.B.: Dynamical systems and non-hermitian iterative eigensolvers. SIAM Journal on Numerical Analysis 47(2), 1445–1473 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Freeman, L.: Centrality in social networks conceptual clarification. Social Networks 1(3), 215–239 (1979)

    Article  Google Scholar 

  15. Gleich, D., Glynn, P., Golub, G., Greif, C.: Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative. In: Web Information Retrieval and Linear Algebra Algorithms (2007)

    Google Scholar 

  16. Grindrod, P., Parsons, M., Higham, D., Estrada, E.: Communicability across evolving networks. Physical Review E 83(4), 046120 (2011)

    Google Scholar 

  17. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  18. Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  19. Langville, A.N., Meyer, C.D.: Updating PageRank with iterative aggregation. In: WWW, pp. 392–393 (2004)

    Google Scholar 

  20. Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press (2006)

    Google Scholar 

  21. Mathieu, F., Bouklit, M.: The effect of the back button in a random walk: application for PageRank. In: WWW, pp. 370–371 (2004)

    Google Scholar 

  22. Morrison, J.L., Breitling, R., Higham, D.J., Gilbert, D.R.: GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinformatics 6(1), 233 (2005)

    Article  Google Scholar 

  23. O’Madadhain, J., Smyth, P.: Eventrank: A framework for ranking time-varying networks. In: LinkKDD, pp. 9–16. ACM (2005)

    Google Scholar 

  24. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web (1998)

    Google Scholar 

  25. Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., Vespignani, A.: Characterizing and modeling the dynamics of online popularity. Physical Review Letters 105(15), 158701 (2010)

    Article  Google Scholar 

  26. Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: dynamic tensor analysis. In: SIGKDD, KDD 2006, pp. 374–383. ACM, New York (2006)

    Google Scholar 

  27. Suzuki, Y., et al.: Identification and characterization of the potential promoter regions of 1031 kinds of human genes. Genome Research 11(5), 677–684 (2001)

    Article  Google Scholar 

  28. Various. Wikipedia database dump, Version from (March 6, 2009), http://en.wikipedia.org/wiki/Wikipedia:Database_download

  29. Various. Wikipedia pageviews (2011), http://dumps.wikimedia.org/other/pagecounts-raw/ (accessed in 2011)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Purdue University, 305 N. University St., West Lafayette, IN, 47906, USA

    Ryan A. Rossi & David F. Gleich

Authors
  1. Ryan A. Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David F. Gleich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics, Ryerson University, M5B 2K3, Toronto, ON, Canada

    Anthony Bonato

  2. Department of Mathematics and Statistics, Dalhousie University, B3H 3J5, Halifax, NS, Canada

    Jeannette Janssen

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rossi, R.A., Gleich, D.F. (2012). Dynamic PageRank Using Evolving Teleportation. In: Bonato, A., Janssen, J. (eds) Algorithms and Models for the Web Graph. WAW 2012. Lecture Notes in Computer Science, vol 7323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30541-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30541-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30540-5

  • Online ISBN: 978-3-642-30541-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation