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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
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 areExpand
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Deeper Inside PageRank
This paper serves as a companion or extension to the "Inside PageRank" paper by Bianchini et al. [Bianchini et al. 03]. It is a comprehensive survey of all issues associated with PageRank, coveringExpand
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A Survey of Eigenvector Methods for Web Information Retrieval
Web information retrieval is significantly more challenging than traditional well-controlled, small document collection information retrieval. One main difference between traditional informationExpand
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A Reordering for the PageRank Problem
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 substitutionExpand
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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
It is well known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms. Many NMF algorithms are sensitive withExpand
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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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Updating Markov Chains with an Eye on Google's PageRank
An iterative algorithm based on aggregation/disaggregation principles is presented for updating the stationary distribution of a finite homogeneous irreducible Markov chain. The focus is onExpand
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Sensitivity and Stability of Ranking Vectors
We conduct an analysis of the sensitivity of three linear algebra-based ranking methods: the Colley, Massey, and Markov methods. Our analysis employs reverse engineering, in that we start with aExpand
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