To randomize or not to randomize: space optimal summaries for hyperlink analysis

  • Authors:
  • Tamás Sarlós;Adrás A. Benczúr;Károly Csalogány;Dániel Fogaras;Balázs Rácz

  • Affiliations:
  • Hungarian Academy of Sciences (MTA SZTAKI) and Eötvös University, Budapest;Hungarian Academy of Sciences (MTA SZTAKI) and Eötvös University, Budapest;Hungarian Academy of Sciences (MTA SZTAKI) and Eötvös University, Budapest;Hungarian Academy of Sciences (MTA SZTAKI) and Budapest University of Technology and Economics;Hungarian Academy of Sciences (MTA SZTAKI) and Budapest University of Technology and Economics

  • Venue:
  • Proceedings of the 15th international conference on World Wide Web
  • Year:
  • 2006

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Abstract

Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching techniques in the dynamic programming algorithm of Jeh and Widom [WWW 2003]. We evaluate the precision of approximation experimentally on large scale real-world data and find significant improvement over previous results. As a key theoretical contribution we show that our algorithms use an optimal amount of space by also improving earlier asymptotic worst-case lower bounds. Our lower bounds and algorithms apply to the SimRank as well; of independent interest is the reduction of the SimRank computation to personalized PageRank.