Author pages are created from data sourced from our academic publisher partnerships and public sources.

- Publications
- Influence

Google's PageRank and beyond - the science of search engine rankings

- A. Langville, C. Meyer
- Computer Science, Mathematics
- 3 July 2006

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? And… Expand

- 1,406
- 146
- PDF

Algorithms and applications for approximate nonnegative matrix factorization

- M. W. Berry, M. Browne, A. Langville, V. P. Pauca, R. Plemmons
- Mathematics, Computer Science
- Comput. Stat. Data Anal.
- 15 September 2007

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 are… Expand

Deeper Inside PageRank

- A. Langville, C. Meyer
- Computer Science
- Internet Math.
- 1 January 2004

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, covering… Expand

A Survey of Eigenvector Methods for Web Information Retrieval

- A. Langville, C. Meyer
- Computer Science, Mathematics
- SIAM Rev.
- 2005

Web information retrieval is significantly more challenging than traditional well-controlled, small document collection information retrieval. One main difference between traditional information… Expand

A Reordering for the PageRank Problem

- A. Langville, C. Meyer
- Computer Science, Mathematics
- SIAM J. Sci. Comput.
- 15 December 2005

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… Expand

Google's PageRank and Beyond

- A. Langville, C. Meyer
- Computer Science
- 2007

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 patented… Expand

Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization

- A. Langville, C. Meyer, R. Albright, J. Cox, David Duling
- Mathematics, Computer Science
- ArXiv
- 27 July 2014

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 with… Expand

Initializations for the Nonnegative Matrix Factorization

- A. Langville, C. Meyer
- 2006

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 and… Expand

- 100
- 6
- PDF

Updating Markov Chains with an Eye on Google's PageRank

- A. Langville, C. Meyer
- Mathematics, Computer Science
- SIAM J. Matrix Anal. Appl.
- 31 December 2005

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 on… Expand

Sensitivity and Stability of Ranking Vectors

- T. Chartier, E. Kreutzer, A. Langville, Kathryn E. Pedings
- Computer Science, Mathematics
- SIAM J. Sci. Comput.
- 1 May 2011

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 a… Expand