Using polynomial chaos to compute the influence of multiple random surfers in the PageRank model

Authors:
Paul G. Constantine;David F. Gleich
Affiliations:
Stanford University, Institute for Computational and Mathematical Engineering;Stanford University, Institute for Computational and Mathematical Engineering
Venue:
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Year:
2007

Citing 11
Cited 2

Stochastic simulation

Stochastic simulation
Stochastic finite elements: a spectral approach

Stochastic finite elements: a spectral approach
WebBase: a repository of Web pages

Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A stochastic projection method for fluid flow. I: basic formulation

Journal of Computational Physics
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

SIAM Journal on Scientific Computing
Extrapolation methods for accelerating PageRank computations

WWW '03 Proceedings of the 12th international conference on World Wide Web
Scaling personalized web search

WWW '03 Proceedings of the 12th international conference on World Wide Web
UbiCrawler: a scalable fully distributed web crawler

Software—Practice & Experience
An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

Journal of Computational Physics
Google's PageRank and Beyond: The Science of Search Engine Rankings

Google's PageRank and Beyond: The Science of Search Engine Rankings
Generalizing PageRank: damping functions for link-based ranking algorithms

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Tracking the random surfer: empirically measured teleportation parameters in PageRank

Proceedings of the 19th international conference on World wide web
Quasi-stationary distributions as centrality measures for the giant strongly connected component of a reducible graph

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The PageRank equation computes the importance of pages in a web graph relative to a single random surfer with a constant teleportation coefficient. To be globally relevant, the teleportation coefficient should account for the influence of all users. Therefore, we correct the PageRank formulation by modeling the teleportation coefficient as a random variable distributed according to user behavior. With this correction, the PageRank values themselves become random. We present two methods to quantify the uncertainty in the random PageRank: a Monte Carlo sampling algorithm and an algorithm based the truncated polynomial chaos expansion of the random quantities. With each of these methods, we compute the expectation and standard deviation of the PageRanks. Our statistical analysis shows that the standard deviation of the PageRanks are uncorrelated with the PageRank vector.