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        <identifier>oai:drops-oai.dagstuhl.de:1069</identifier>
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          <dc:title>Google Pageranking Problem: The Model and the Analysis</dc:title>
          <dc:creator>Serra Capizzano, Stefano</dc:creator>
          <dc:subject>Google matrix</dc:subject>
          <dc:subject>rank-one perturbation</dc:subject>
          <dc:subject>Jordan canonical form</dc:subject>
          <dc:subject>extrapolation formulae.</dc:subject>
          <dc:description>Let $A$ be a given $n$-by-$n$ complex matrix with eigenvalues $lambda&#13;
,lambda _{2},ldots ,lambda _{n}$. Suppose there are nonzero vectors $%&#13;
x,yin mathbb{C}^{n}$ such that $Ax=lambda x$, $y^{ast }A=lambda y^{ast&#13;
}$, and $y^{ast }x=1$. Let $vin mathbb{C}^{n}$ be such that $v^{ast }x=1$%&#13;
, let $cin mathbb{C}$, and assume that $lambda &#13;
eq clambda _{j}$ for&#13;
each $j=2,ldots ,n$. Define $A(c):=cA+(1-c)lambda xv^{ast }$. The eigenvalues of $%&#13;
A(c)$ are $lambda ,clambda _{2},ldots ,clambda _{n}$. Every&#13;
left eigenvector of $A(c)$ corresponding to $lambda $ is a scalar multiple of $%&#13;
y-z(c)$, in which the vector $z(c)$ is an explicit rational&#13;
function of $c$. If a standard form such as the Jordan canonical&#13;
form or the Schur triangular form is known for $A$, we show how to&#13;
obtain the corresponding standard form of $A(c)$.&#13;
&#13;
The web hyper-link matrix $G(c)$ used by Google for computing the&#13;
PageRank is a special case in which $A$ is real, nonnegative, and&#13;
row stochastic (taking into consideration the dangling nodes),&#13;
$cin (0,1)$, $x$ is the vector of all ones, and $v$ is a positive&#13;
probability vector. The PageRank vector (the normalized dominant&#13;
left eigenvector of $G(c)$) is therefore an explicit rational&#13;
function of $c$. Extrapolation procedures on the complex field may&#13;
give a practical and efficient way to compute the PageRank vector&#13;
when $c$ is close to $1$.&#13;
&#13;
A discussion on the model, on its adherence to reality, and on&#13;
possible variations is also considered.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Stefano Serra Capizzano</dc:contributor>
          <dc:date>2007</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 7071, Web Information Retrieval and Linear Algebra Algorithms (2007)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/DagSemProc.07071.10</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-10693</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07071.10</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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