Internet Mathematics

Approximating Personalized PageRank with Minimal Use of Web Graph Data

David Gleich and Marzia Polito

Full-text: Open access

Abstract

In this paper, we consider the problem of calculating fast and accurate approximations to the personalized PageRank score of a webpage. We focus on techniques to improve speed by limiting the amount of web graph data we need to access.

Our algorithms provide both the approximation to the personalized PageRank score as well as guidance in using only the necessary information—and therefore sensibly reduce not only the computational cost of the algorithm but also the memory and memory bandwidth requirements. We report experiments with these algorithms on web graphs of up to 118 million pages and prove a theoretical approximation bound for all. Finally, we propose a local, personalized web-search system for a future client system using our algorithms.

Article information

Source
Internet Math., Volume 3, Number 3 (2006), 257-294.

Dates
First available in Project Euclid: 7 March 2008

Permanent link to this document
https://projecteuclid.org/euclid.im/1204906158

Mathematical Reviews number (MathSciNet)
MR2372544

Zentralblatt MATH identifier
1147.68350

Citation

Gleich, David; Polito, Marzia. Approximating Personalized PageRank with Minimal Use of Web Graph Data. Internet Math. 3 (2006), no. 3, 257--294. https://projecteuclid.org/euclid.im/1204906158


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