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Detecting colluders in PageRank: Fin...

Mason, Kahn.

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Detecting colluders in PageRank: Finding slow mixing states in a Markov chain.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detecting colluders in PageRank: Finding slow mixing states in a Markov chain./
Author:	Mason, Kahn.
Description:	75 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3044.
Contained By:	Dissertation Abstracts International66-08A.
Subject:	Economics, Theory. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3187317
ISBN:	9780542295676

Detecting colluders in PageRank: Finding slow mixing states in a Markov chain.
Mason, Kahn.

Detecting colluders in PageRank: Finding slow mixing states in a Markov chain. - 75 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3044.

Thesis (Ph.D.)--Stanford University, 2005.

The PageRank algorithm evaluates webpage reputations based on the hyperlinks that connect them. Webpages that collude to boost their reputations significantly distort the resulting rankings. We introduce a measure for assessing the degree to which a set of webpages boosts its reputation. There is no known efficient algorithm that is guaranteed to detect significantly boosted sets when they exist. However, we provide metrics that, under reasonable conditions, are guaranteed to detect a member of a significantly boosted set, if one exists, and address various implementation issues that arise in incorporating these metrics into PageRank.

ISBN: 9780542295676Subjects--Topical Terms:

1017575
Economics, Theory.

Detecting colluders in PageRank: Finding slow mixing states in a Markov chain.
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100 1 $a Mason, Kahn. $3 1916861
245 1 0 $a Detecting colluders in PageRank: Finding slow mixing states in a Markov chain.
300 $a 75 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: A, page: 3044.
500 $a Adviser: Benjamin Van Roy.
502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 $a The PageRank algorithm evaluates webpage reputations based on the hyperlinks that connect them. Webpages that collude to boost their reputations significantly distort the resulting rankings. We introduce a measure for assessing the degree to which a set of webpages boosts its reputation. There is no known efficient algorithm that is guaranteed to detect significantly boosted sets when they exist. However, we provide metrics that, under reasonable conditions, are guaranteed to detect a member of a significantly boosted set, if one exists, and address various implementation issues that arise in incorporating these metrics into PageRank.
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