Internet Mathematics

Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments

Károly Csalogány, Dániel Fogaras, Balázs Rácz, and Tamás Sarlós

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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.

Article information

Source
Internet Math., Volume 2, Number 3 (2005), 333-358.

Dates
First available in Project Euclid: 16 June 2006

Permanent link to this document
https://projecteuclid.org/euclid.im/1150474886

Mathematical Reviews number (MathSciNet)
MR2212369

Zentralblatt MATH identifier
1095.68579

Citation

Fogaras, Dániel; Rácz, Balázs; Csalogány, Károly; Sarlós, Tamás. Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments. Internet Math. 2 (2005), no. 3, 333--358. https://projecteuclid.org/euclid.im/1150474886


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