- Avrachenkov, K., Litvak, N., and Pham, K. S. 2007. Distribution of PageRank mass among principle components of the Web. In Proceedings of the 5th International Workshop on Algorithms and Models for the Web-Graph (WAW'07), A. Bonato and F. R. K. Chung, Eds. Lecture Notes in Computer Science, vol. 4863. Springer, 16--28. Google ScholarDigital Library
- Baeza-Yates, R., Boldi, P., and Castillo, C. 2006. Generalizing PageRank: Damping functions for link-based ranking algorithms. In (SIGIR '06) Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, 308--315. Google ScholarDigital Library
- Bao, Y. and Liu, Y. 2006. Limit of PageRank with damping factor. Dynam. Contin. Discr. Impulsive Syst. 13, 497--504.Google Scholar
- Boldi, P., Codenotti, B., Santini, M., and Vigna, S. 2004. Ubicrawler: A scalable fully distributed Web crawler. Softw. Pract. Exper. 34, 8, 711--726. Google ScholarDigital Library
- Boldi, P., Lonati, V., Santini, M., and Vigna, S. 2006. Graph fibrations, graph isomorphism, and PageRank. RAIRO Inf. Théor. 40, 227--253.Google ScholarCross Ref
- Boldi, P., Posenato, R., Santini, M., and Vigna, S. 2008. Traps and pitfalls of topic-biased Page Rank. In Proceedings of the 4th Workshop on Algorithms and Models for the Web-Graph (WAW '06), W. Aiello, A. Broder, J. Janssen, and E. Milios, Eds. Lecture Notes in Computer Science, vol. 4936. Springer, 107--116. Google ScholarDigital Library
- Boldi, P., Santini, M., and Vigna, S. 2005. PageRank as a function of the damping factor. In Proceedings of the 14th International World Wide Web Conference. ACM Press, 557--566. Google ScholarDigital Library
- Boldi, P. and Vigna, S. 2004. The WebGraph framework I: Compression techniques. In Proceedings of the 13th International World Wide Web Conference. ACM Press, 595--601. Google ScholarDigital Library
- Brezinski, C. and Redivo-Zaglia, M. 2006. The PageRank vector: Properties, computation, approximation, and acceleration. SIAM J. Matrix Anal. Appl. 28, 2, 551--575. Google ScholarDigital Library
- Brinkmeier, M. 2006. PageRank revisited. ACM Trans. Internet Technol. 6, 3, 282--301. Google ScholarDigital Library
- Del Corso, G., Gullì, A., and Romani, F. 2006. Fast PageRank computation via a sparse linear system. Internet Math. 2, 3, 251--273.Google ScholarCross Ref
- Eiron, N., McCurley, K. S., and Tomlin, J. A. 2004. Ranking the Web frontier. In Proceedings of the 13th International World Wide Web Conference. ACM Press, 309--318. Google ScholarDigital Library
- Fogaras, D. 2005. Personal communication.Google Scholar
- Golub, G. H. and Greif, C. 2006. An Arnoldi-type algorithm for computing PageRank. BIT Numer. Math. 46, 4, 759--771.Google ScholarCross Ref
- Haveliwala, T. and Kamvar, S. 2003a. The condition number of the PageRank problem. Tech. rep. 36, Stanford University.Google Scholar
- Haveliwala, T. H. 1999. Efficient computation of PageRank. Tech. rep. 31, Stanford University.Google Scholar
- Haveliwala, T. H. and Kamvar, S. D. 2003b. The second eigenvalue of the Google matrix. Tech. rep. 20, Stanford University.Google Scholar
- Iosifescu, M. 1980. Finite Markov Processes and Their Applications. John Wiley&Sons.Google Scholar
- Jeh, G. and Widom, J. 2003. Scaling personalized Web search. In Proceedings of the 12th International World Wide Web Conference. ACM Press, 271--279. Google ScholarDigital Library
- Kamvar, S. D., Haveliwala, T. H., Manning, C. D., and Golub, G. H. 2003. Extrapolation methods for accelerating PageRank computations. In Proceedings of the 12th International World Wide Web Conference. ACM Press, 261--270. Google ScholarDigital Library
- Katz, L. 1953. A new status index derived from sociometric analysis. Psychometrika 18, 1, 39--43.Google ScholarCross Ref
- Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tompkins, A., and Upfal, E. 2000. The Web as a graph. In Proceedings of the 19th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS '00). ACM Press, 1--10. Google ScholarDigital Library
- Langville, A. N. and Meyer, C. D. 2004. Deeper inside PageRank. Internet Math. 1, 3, 355--400.Google ScholarCross Ref
- Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The PageRank citation ranking: Bringing order to the Web. Tech. rep. 66, Stanford University.Google Scholar
- Pinski, G. and Narin, F. 1976. Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Inf. Proces. Manag. 12, 5, 297--312.Google ScholarCross Ref
- Pretto, L. 2002a. A theoretical analysis of Google's PageRank. In Proceedings of the 9th International Symposium on String Processing and Information Retrieval (SPIRE '02), A. H. F. Laender and A. L. Oliveira, Eds. Lecture Notes in Computer Science, vol. 2476. Springer, 125--136. Google ScholarDigital Library
- Pretto, L. 2002b. A theoretical approach to link analysis algorithms. Ph.D. thesis.Google Scholar
- Serra-Capizzano, S. 2005. Jordan canonical form of the Google matrix: A potential contribution to the PageRank computation. SIAM J. Matrix Anal. Appl. 27, 2, 305--312. Google ScholarDigital Library
- Vigna, S. 2005. TruRank: Taking PageRank to the limit. In Proceedings of the 14th International World Wide Web Conference (WWW 2005), Special Interest Tracks&Posters. ACM Press, 976--977. Google ScholarDigital Library
- Vigna, S. 2007. Stanford matrix considered harmful. In Web Information Retrieval and Linear Algebra Algorithms, A. Frommer, M. W. Mahoney, and D. B. Szyld, Eds. Number 07071 in Dagstuhl Seminar Proceedings. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.Google Scholar
Index Terms
- PageRank: Functional dependencies
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