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Indegree and PageRank: Why do they follow similar power laws
"... PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that PageRank values obey a power law with the same exponent as InDegree values. This paper presents a novel mathematical model that explains this phenomenon. The relation between PageRank and InDegree is mo ..."
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Cited by 9 (5 self)
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PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that PageRank values obey a power law with the same exponent as InDegree values. This paper presents a novel mathematical model that explains this phenomenon. The relation between PageRank and InDegree
TopicSensitive PageRank
, 2002
"... In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search resu ..."
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Cited by 535 (10 self)
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In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search
InDegree and PageRank of Web Pages: Why Do They Follow Similar Power Laws?” Memorandum 1807, Dept
 of Applied Math., Univ. of Twente
, 2006
"... The PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that the PageRank obeys a ‘power law ’ with the same exponent as the InDegree. This paper presents a novel mathematical model that explains this phenomenon. The relation between the PageRank and InDegree ..."
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Cited by 5 (1 self)
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The PageRank is a popularity measure designed by Google to rank Web pages. Experiments confirm that the PageRank obeys a ‘power law ’ with the same exponent as the InDegree. This paper presents a novel mathematical model that explains this phenomenon. The relation between the PageRank and InDegree
ISSN 01692690InDegree and PageRank of Web pages:
, 2006
"... www.math.utwente.nl/publications ..."
Local Approximation of PageRank and Reverse PageRank
"... We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. This problem was originally studied by Chen, Gan, and Suel (CIKM 2004), who presented an algorithm for tackling it. We prove that local approximation of PageRank, even to wi ..."
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Cited by 12 (0 self)
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bounded indegree and admits fast PageRank convergence, then local PageRank approximation can be done using a small number of queries. Unfortunately, natural graphs, such as the web graph, are abundant with high indegree nodes, making this algorithm (or any other local approximation algorithm) too costly
Using PageRank to Characterize Web Structure
"... Recent work on modeling the web graph has dwelt on capturing the degree distributions observed on the web. Pointing out that this represents a heavy reliance on “local” properties of the web graph, we study the distribution of PageRank values on the web. Our measurements suggest that PageRank value ..."
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Cited by 114 (0 self)
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Recent work on modeling the web graph has dwelt on capturing the degree distributions observed on the web. Pointing out that this represents a heavy reliance on “local” properties of the web graph, we study the distribution of PageRank values on the web. Our measurements suggest that PageRank
Experiments with PageRank computation
, 2004
"... PageRank algorithm is one of the most commonly used algorithms that determine the global importance of web pages. Due to the size of web graph which contains billions of nodes, computing a PageRank vector is very computational intensive and it may takes any time between months to hours depending on ..."
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Cited by 7 (0 self)
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PageRank algorithm is one of the most commonly used algorithms that determine the global importance of web pages. Due to the size of web graph which contains billions of nodes, computing a PageRank vector is very computational intensive and it may takes any time between months to hours depending
PageRank: Functional Dependencies
"... PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 ..."
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Cited by 15 (5 self)
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derivatives of any order, and by proving that the kth iteration of the Power Method gives exactly the value obtained by truncating the PageRank power series at degree k, we show how to obtain an approximation of the derivatives. Finally, we view PageRank as a linear operator acting on the preference vector
PageRank for Bibliographic Networks
"... In this paper, we present several modifications of the classical PageRank formula adapted for bibliographic networks. Our versions of PageRank take into account not only the citation but also the coauthorship graph. We verify the viability of our algorithms by applying them to the data from the DBL ..."
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Cited by 15 (5 self)
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the DBLP digital library and by comparing the resulting ranks of the winners of the ACM E. F. Codd Innovations Award. Rankings based on both the citation and coauthorship information turn out to be “better ” than the standard PageRank ranking.
Powerlaw distributions in empirical data
 ISSN 00361445. doi: 10.1137/ 070710111. URL http://dx.doi.org/10.1137/070710111
, 2009
"... Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the t ..."
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Cited by 589 (7 self)
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estimates for powerlaw data, based on maximum likelihood methods and the KolmogorovSmirnov statistic. We also show how to tell whether the data follow a powerlaw distribution at all, defining quantitative measures that indicate when the power law is a reasonable fit to the data and when it is not. We
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