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Local approximation of pagerank and reverse pagerank
 in CIKM
, 2008
"... ABSTRACT 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 several algorithms for tackling it. We prove that local approximation of Pag ..."
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Cited by 11 (0 self)
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of PageRank, even to within modest approximation factors, is infeasible in the worstcase, as it requires probing the link server for Ω(n) nodes, where n is the size of the graph. The difficulty emanates from nodes of high indegree and/or from slow convergence of the PageRank random walk. We show
MULTILINEAR PAGERANK∗
"... Abstract. In this paper, we first extend the celebrated PageRank modification to a higherorder Markov chain. Although this system has attractive theoretical properties, it is computationally intractable for many interesting problems. We next study a computationally tractable approximation to the hi ..."
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is an instance of a vertexreinforced random walk. We develop convergence theory for a simple fixedpoint method, a shifted fixedpoint method, and a Newton iteration in a particular parameter regime. In marked contrast to the case of the PageRank vector of a Markov chain where the solution is always unique
structures: The PageRank case
"... Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires pri ..."
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Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires prior specific permission by the publisher mentioned above.
Towards scaling fully personalized PageRank
 In Proceedings of the 3rd Workshop on Algorithms and Models for the WebGraph (WAW
, 2004
"... Abstract Personalized PageRank expresses backlinkbased page quality around userselected pages in a similar way as PageRank expresses quality over the entire Web. Existing personalized PageRank algorithms can however serve online queries only for a restricted choice of page selection. In this pape ..."
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Cited by 104 (2 self)
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pages. We justify our estimation approach by asymptotic worstcase lower bounds; we show that exact personalized PageRank values can only be obtained from a database of quadratic size. 1
Local computation of pagerank contributions
 In WAW
, 2007
"... Abstract. Motivated by the problem of detecting linkspam, we consider the following graphtheoretic primitive: Given a webgraph G, a vertex v in G, and a parameter δ ∈ (0, 1), compute the set of all vertices that contribute to v at least a δ fraction of v’s PageRank. We call this set the δcontribu ..."
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Cited by 30 (12 self)
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set of k vertices that contribute at least a (ρ − ɛ)fraction to the PageRank of v. In this case, we prove that our algorithm examines at most O(k/ɛ) vertices. 1
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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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
Fast Incremental and Personalized PageRank
"... In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on largescale dynamically evolving social networks. We assume that the graph of friendships is stored in distr ..."
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Cited by 36 (3 self)
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in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. We show that with a reset probability of, the expected total work needed to maintain an accurate
PageRank as a Function of the Damping Factor
, 2005
"... 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 # that spreads uniformly part of the rank. The choice of # is eminently empirical, and in most cases the original suggestion # = 0.85 ..."
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Cited by 60 (11 self)
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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 # that spreads uniformly part of the rank. The choice of # is eminently empirical, and in most cases the original suggestion # = 0
An Improved Computation of the PageRank Algorithm
, 2002
"... The Google search site (http://www.google.com) exploits the link structure of the Web to measure the relative importance of Web pages. The ranking method implemented in Google is called PageRank [3]. The sum of all PageRank values should be one. However, we notice that the sum becomes less than ..."
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Cited by 12 (0 self)
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one in some cases. We present an improved PageRank algorithm that computes the PageRank values of the Web pages correctly. Our algorithm works out well in any situations, and the sum of all PageRank values is always maintained to be one. We also present implementation issues of the improved
BackRank: an Alternative for PageRank?
"... This paper proposes to extend a previous work, The Effect of the Back Button in a Random Walk: Application for PageRank [5]. We introduce an enhanced version of the PageRank algorithm using a realistic model for the Back button, thus improving the random surfer model. We show that in the special cas ..."
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Cited by 7 (0 self)
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case where the history is bound to an unique page (you cannot use the Back button twice in a row), we can produce an algorithm that does not need much more resources than a standard PageRank. This algorithm, BackRank, can converge up to 30% faster than a standard PageRank and suppress most
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