Deeper Inside PageRank

By Amy Nicole Langville, Carl Dean Meyer. 2004

1,033 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

GenDICE: Generalized Offline Estimation of Stationary Values (2020, background): “Famously, the steady-state distribution of a random walk on the World Wide Web provides the foundation of the PageRank algorithm (Langville & Meyer, 2004).”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, methodology): “A formal analysis on the convergence of random walks on these graphs is documented in [34].”
Combating Web Spam with TrustRank (2004, background): “Recent analyses of (biased) PageRank are provided by [2, 11].”
A Survey on PageRank Computing (2005, methodology): “…transformations (2.3-2.4), since the multiplication with matrix P 00 can be carried out [Kamvar et al., 2003c] in terms of the original P : the vector y = Ax = P T x can be computed by the following code General information related to PageRank can be found in a survey [Langville and Meyer, 2004a].”
Personalizing PageRank for Word Sense Disambiguation (2009, background): “For the sake of brevity we omit the details, which the interested reader can check in (Langville and Meyer, 2003).”
PageRank beyond the Web (2014, background): “For instance, Langville and Meyer [2004] advocates transitioning uniformly from dangling nodes.”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, methodology): “Instead, SybilRank uses power iteration [28] to compute the random walk landing probability for all nodes.”
Mining on Manifolds: Metric Learning Without Labels (2018, methodology): “This is achieved by the power iteration method [28] yielding the leading left eigenvector of the transition matrix P = D−1A, such that πP = π.”
Adversarial Web Search (2011, background): “For surveys on many aspects of PageRank, see Langville and Meyer [138, 139].”
ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines (2007, methodology): “Biased PageRank has been analysed in [Langville and Meyer, 2003] and custom static score distribution vectors d have been applied to compute topic-sensitive PageRank [Haveliwala, 2002], reputation of a node in a peerto-peer network [Kamvar et al.”
Fast Incremental and Personalized PageRank (2010, methodology): “A great survey of many of the methods in the ﬁrst category is done by Langville and Meyer [26].”
Mining significant semantic locations from GPS data (2010, background): “They can be understood as as a Markov chain in which the states are pages and the transition probabilities are determined by the links between pages; the PageRank problem actually amounts to solving an old problem (computing the stationary vector of a Markov chain) in the context of web links [12].”
A Survey of Eigenvector Methods for Web Information Retrieval (2005, background): “Moreover, as α → 1, not only does convergence slow drastically, but sensitivity issues begin to surface as well [39].”
Recommendation Networks and the Long Tail of Electronic Commerce (2010, neutral): “For further details and extensions, see Langville and Meyer (2005).”
Reweighted Random Walks for Graph Matching (2010, methodology): “Internet Democracy: each webpage has a total of one vote [19].”