Monte Carlo methods in pagerank computation: When one iteration is sufficient

SIAM Journal on Numerical Analysis

Volumn 45, Issue 2, 2007, Pages 890-904

  Avrachenkov, K ^a   Litvak, N ^b   Nemirovsky, D ^c   Osipova, N ^a  

^a INRIA   (France)

^b UNIVERSITY OF TWENTE   (Netherlands)

^c ST PETERSBURG STATE UNIVERSITY   (Russian Federation)

Author keywords

Absorbing Markov chains; Google; Monte Carlo methods; PageRank

Indexed keywords

COMPUTATION THEORY; ITERATIVE METHODS; MONTE CARLO METHODS; SEARCH ENGINES;

GOOGLE; PAGERANK; POWER ITERATION METHOD;

WEBSITES;

EID: 38149027373     PISSN: 00361429     EISSN: None     Source Type: Journal    
DOI: 10.1137/050643799     Document Type: Article

References (17)

1. S. ABITEBOUL, M. PREDA, AND G. COBENA, Adaptive on-line page importance computation, in Proceedings of the 12th International World Wide Web Conference, Budapest, 2003.
2. M. BIANCHINI, M. GORI, AND F. SCARSELLI, Inside PageRank, ACM Trans. Internet Technology, 5 (2005), pp. 92-128.
3. L. A. BREYER, Markovian Page Ranking Distributions: Some Theory and Simulations, Technical report, 2002; available online at http://www.lbreyer.com/preprints.html.
4. L. A. BREYER AND G. O. ROBERTS, Catalytic perfect simulation, Methodol. Comput. Appl. Probab., 3 (2001), pp. 161-177.
5. S. BRIN, L. PAGE, R. MOTWANI, AND T. WINOGRAD, The PageRank Citation Ranking: Bringing Order to the Web, Stanford University Technical Report, Stanford, CA, 1998; available online at http://dbpubs.stanford.edu:8090/pub/1999-66.
6. A. BRODER, R. KUMAR, F. MAGHOUL, P. RAGHAVAN, S. RAJAGOPALAN, R. STATA, A. TOMKINS, AND J. WIENER, Graph structure in the Web, Computer Networks, 33 (2000), pp. 309-320.
7. S. DILL, R. KUMAR, K. MCCURLEY. S. RAJAGOPALAN, D. SIVAKUMAR. AND A. TOMKINS, Self-similarity in the Web, ACM Trans. Internet Technol., 2 (2002), pp. 205-223.
8. D. FOCARAS AND B. RACZ, Towards scaling fully personalized PageRank, in Proceedings of the 3rd International Workshop on Algorithms and Models for the Web-Graph, New York, 2004.
9. I. C. F. IPSEN AND S. KIRKLAND, Convergence Analysis of an Improved PageRank Algorithm, North Carolina State University Technical Report CRSC-TR04-02, 2004; available online at http://www.ncsu.edu/ crsc/reports/reports04.html.
10. G. JEH AND J. WIDOM, Scaling personalized web search, in Proceedings of the 12th World Wide Web Conference, Budapest, 2003.
11. M. R. HENZINGER, Algorithmic challenges in web search engines, Internet Mathematics, 1 (2003), pp. 115-126.
12. S. D. KAMVAR, T. H. HAVELIWALA, AND G. H. GOLUB, Adaptive methods for the computation of PageRank, Linear Algebra Appl., 386 (2004), pp. 51-65.
13. S. D. KAMVAR, T. H. HAVELIWALA, C D. MANNING, AND G. H. GOLUB, Extrapolation methods for accelerating PageRank computations, in Proceedings of the 12th International World Wide Web Conference, Budapest, 2003.
14. J. KLEINBERG, S. R. KUMAR. P. RAGHAVAN. S. RAJAGOPALAN, AND A. TOMKINS, The Web as a Graph: Measurements, Models, and Methods, Invited Survey at the International Conference on Combinatorics and Computing, Tokyo, 1999; available online at http://www.cs.cornell.edu/home/kleinber/web-graph.ps.
15. A. N. LANGVILLE AND C. D. MEYER, Deeper Inside PageRank, Internet Math., 1 (2004), pp. 335-400; also available online at http://www4.ncsu.edu/~anlangvi/.
16. A. N. LANGVILLE AND C. D. MEYER, Updating PageRank with iterative aggregation, in Proceedings of the 13th World Wide Web Conference, New York, 2004.
17. Google Dance-The Index Update of the Google Search Engine, http://dance.efactory.de.