Adaptive methods for the computation of PageRank
By Sepandar D. Kamvar, Taher H. Haveliwala, Gene H. Golub. 2004
334 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/ff20b4a37fd56414c57a246c3898fc8f8a99fcf9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- J. Demmel
- Bahman Bahmani
- Ashish Goel
- Abdur Chowdhury
- Hengshuai Yao
- R. Vuduc
- Dale Schuurmans
- Carl Dean Meyer
In Computer Networks and Communications
- J. Demmel
- Ashish Goel
- Bahman Bahmani
- R. Vuduc
- Abdur Chowdhury
- Dale Schuurmans
- Carl Dean Meyer
- Amy Nicole Langville
In Information Systems
- Bahman Bahmani
- Ashish Goel
- Abdur Chowdhury
- Carl Dean Meyer
- Amy Nicole Langville
- J. Demmel
- Dale Schuurmans
- R. Vuduc
Citations in the corpus
- Automatic performance tuning of sparse matrix kernels (2003, background): “The structure of this matrix is very special, and there have been a number of early characterizations of the structure of this matrix [185, 151, 184, 221, 244].”
- Fast Incremental and Personalized PageRank (2010, methodology): “A family of the methods proposed in this category deal with accelerating the basic power iteration method for computing PageRank values [20–22].”
- Reinforcement Ranking (2013, methodology): “An efficient procedure for computing Page Rank is given in Algorithm 1 [22, 23].”
- Deeper Inside PageRank (2004, methodology): “The first method was proposed by Kamvar et al. [69] and is called adaptive PageRank.”