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Google's PageRank and beyond - the science of search engine rankings
Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other web pages always appear at the top? What creates these powerful rankings? AndExpand
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Algorithms and applications for approximate nonnegative matrix factorization
TLDR
The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. Expand
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Deeper Inside PageRank
TLDR
We present a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties. Expand
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A Survey of Eigenvector Methods for Web Information Retrieval
TLDR
We focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA. Expand
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A Reordering for the PageRank Problem
TLDR
We describe a reordering particularly suited to the pageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. Expand
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Google's PageRank and Beyond
Why is Google so good at what it does? There ate a variety of reasons, but the fundamental thing that distinguishes Google and has put them so far ahead of other search engines is their patentedExpand
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Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization
TLDR
It is well known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization algorithms, including the two new ALS algorithms that we present in this paper. Expand
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Updating Markov Chains with an Eye on Google's PageRank
TLDR
An iterative algorithm based on aggregation/disaggregation principles is presented for updating the stationary distribution of a finite homogeneous irreducible Markov chain. Expand
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Initializations for the Nonnegative Matrix Factorization
The need to process and conceptualize large sparse matrices effectively and efficiently (typically via low-rank approximations) is essential for many data mining applications, including document andExpand
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Sensitivity and Stability of Ranking Vectors
TLDR
We conduct an analysis of the sensitivity of three linear algebra-based ranking methods: the PageRank, Colley, Massey, and Markov methods, and a perturbation analysis of their rank stability. Expand
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