Efficient Parallel Computation of PageRank

  • Christian Kohlschütter
  • Paul-Alexandru Chirita
  • Wolfgang Nejdl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


PageRank inherently is massively parallelizable and distributable, as a result of web’s strict host-based link locality. We show that the Gauß-Seidel iterative method can actually be applied in such a parallel ranking scenario in order to improve convergence. By introducing a two-dimensional web model and by adapting the PageRank to this environment, we present efficient methods to compute the exact rank vector even for large-scale web graphs in only a few minutes and iteration steps, with intrinsic support for incremental web crawling, and without the need for page sorting/reordering or for sharing global rank information.


Link Structure Outgoing Link PageRank Algorithm Jacobi Iteration Target Page 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arasu, A., Novak, J., Tomkins, A., Tomlin, J.: PageRank computation and the structure of the web: Experiments and algorithms (2001)Google Scholar
  2. 2.
    Bharat, K., Chang, B.-W., Henzinger, M.R., Ruhl, M.: Who links to whom: Mining linkage between web sites. In: Proc. of the IEEE Intl. Conf. on Data Mining, pp. 51–58 (2001)Google Scholar
  3. 3.
    Brin, S., Motwani, R., Page, L., Winogradp, T.: What can you do with a web in your pocket? Data Engineering Bulletin 21(2), 37–47 (1998)Google Scholar
  4. 4.
    Broder, A.Z., Lempel, R., Maghoul, F., Pedersen, J.: Efficient pagerank approximation via graph aggregation. In: Proc. of the 13th International World Wide Web Conference, pp. 484–485 (2004)Google Scholar
  5. 5.
    Chen, Y.-Y., Gan, Q., Suel, T.: I/o-efficient techniques for computing pagerank (2002)Google Scholar
  6. 6.
    Cho, J., Garcia-Molina, H.: The evolution of the web and implications for an incremental crawler. In: Proceedings of the 26th International Conference on Very Large Databases (2000)Google Scholar
  7. 7.
    Eiron, N., McCurley, K.S., Tomlin, J.A.: Ranking the web frontier. In: Proc. of the 13th Intl. Conf. on the World Wide Web, pp. 309–318 (2004)Google Scholar
  8. 8.
    Gleich, D., Zhukov, L., Berkhin, P.: Fast parallel PageRank: A linear system approach. Technical report, Yahoo! Research Labs (2004)Google Scholar
  9. 9.
    Haveliwala, T.H.: Efficient computation of PageRank. Technical Report 1999-31, Stanford Library Technologies Project (1999)Google Scholar
  10. 10.
    Kamvar, S., Haveliwala, T., Manning, C., Golub, G.: Exploiting the block structure of the web for computing PageRank. Technical report, Stanford University (2003)Google Scholar
  11. 11.
    Kamvar, S.D., Haveliwala, T.H., Golub, G.H.: Adaptive methods for the computation of PageRank. Technical report, Stanford University (2003)Google Scholar
  12. 12.
    Kamvar, S.D., Haveliwala, T.H., Manning, C.D., Golub, G.H.: Extrapolation methods for accelerating PageRank computations. In: Proc. of the 12th Intl. Conf. on the World Wide Web, pp. 261–270 (2003)Google Scholar
  13. 13.
    Kim, S.J., Lee, S.H.: An improved computation of the pageRank algorithm. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, p. 73. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Koester, D.P., Ranka, S., Fox, G.C.: A parallel gauss-seidel algorithm for sparse power system matrices. In: Proc. of the ACM/IEEE Conf. on Supercomputing, pp. 184–193 (1994)Google Scholar
  15. 15.
    Langville, A.N., Meyer, C.D.: Deeper inside PageRank (2004)Google Scholar
  16. 16.
    Lee, C.P., Golub, G.H., Zenios, S.A.: A fast two-stage algorithm for computing PageRank. Technical report, Stanford University (2003)Google Scholar
  17. 17.
    Manaskasemsak, B., Rungsawang, A.: Parallel PageRank computation on a gigabit pc cluster. In: Proc. of the 18th International Conference on Advanced Information Networking and Application (AINA 2004) (2004)Google Scholar
  18. 18.
    McSherry, F.: A uniform approach to accelerated pagerank computation. In: Proc. of the 14th international conference on World Wide Web, pp. 575–582. ACM Press, New York (2005)Google Scholar
  19. 19.
    Netcraft. Web server survey (2005)Google Scholar
  20. 20.
    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
  21. 21.
    Sankaralingam, K., Sethumadhavan, S., Browne, J.C.: Distributed pagerank for p2p systems. In: Proc. of the 12th IEEE Intl. Symp. on High Performance Distributed Computing (HPDC), p. 58 (2003)Google Scholar
  22. 22.
    Shi, S.-M., Yu, J., Yang, G.-W., Wang, D.-X.: Distributed page ranking in structured p2p networks. In: Proc. of the 2003 International Conference on Parallel Processing (ICPP 2003), pp. 179–186 (2003)Google Scholar
  23. 23.
    Haveliwala, T.H., et al.: 2001 Crawl of the WebBase project (2001)Google Scholar
  24. 24.
    Wang, Y., DeWitt, D.J.: Computing PageRank in a distributed internet search system. In: Proceedings of the 30th VLDB Conference (2004)Google Scholar
  25. 25.
    Wu, J., Aberer, K.: Using SiteRank for P2P Web Retrieval (March 2004)Google Scholar
  26. 26.
    Zhu, Y., Ye, S., Li, X.: Distributed pagerank computation based on iterative aggregation-disaggregation methods. In: Proc. of the 14th ACM international conference on Information and knowledge management (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian Kohlschütter
    • 1
  • Paul-Alexandru Chirita
    • 1
  • Wolfgang Nejdl
    • 1
  1. 1.L3S Research Center/University of HanoverHanoverGermany

Personalised recommendations