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2005 Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments
Károly Csalogány, Dániel Fogaras, Balázs Rácz, Tamás Sarlós
Internet Math. 2(3): 333-358 (2005).

Abstract

Personalized PageRank expresses link-based page quality around userselected pages in a similar way as PageRank expresses quality over the entire web. Existing personalized PageRank algorithms can, however, serve online queries only for a restricted choice of pages. In this paper we achieve full personalization by a novel algorithm that precomputes a compact database; using this database, it can serve online responses to arbitrary user-selected personalization. The algorithm uses simulated random walks; we prove that for a fixed error probability the size of our database is linear in the number of web pages. We justify our estimation approach by asymptotic worst-case lower bounds: we show that on some sets of graphs, exact personalized PageRank values can only be obtained from a database of size quadratic in the number of vertices. Furthermore, we evaluate the precision of approximation experimentally on the Stanford WebBase graph.

Citation

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Károly Csalogány. Dániel Fogaras. Balázs Rácz. Tamás Sarlós. "Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments." Internet Math. 2 (3) 333 - 358, 2005.

Information

Published: 2005
First available in Project Euclid: 16 June 2006

zbMATH: 1095.68579
MathSciNet: MR2212369

Rights: Copyright © 2005 A K Peters, Ltd.

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Vol.2 • No. 3 • 2005
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