Exploiting the Block Structure of the Web for Computing

By Sepandar D. Kamvar, Taher H. Haveliwala, Christopher D. Manning, Gene H. Golub. 2003

413 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/56c65b9bcc0ff1dbbc7b355332d688608e775a17

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Citations in the corpus

Automatic identification of user interest for personalized search (2006, methodology): “For example, [5, 13, 14] provide a framework to limit the bias vector space during the computation of PageRanks, so that acceptable performance can be achieved.”
Deeper Inside PageRank (2004, methodology): “The same group of Stanford researchers, Kamvar et al. [70] has produced one more contribution to the acceleration of PageRank.”
Random walk with restart: fast solutions and applications (2008, methodology): “7 We also perform experiment on BlockRank [ 18 ].”
Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds, and Experiments (2005, methodology): “Earlier personalized PageRank algorithms restricted personalization to a few topics [Haveliwala 02], to a subset of popular pages [Jeh and Widom 03], or to hosts [Kamvar et al. 03a]; see [Haveliwala et al. 03] for an analytical comparison of these methods.”
Twister: a runtime for iterative MapReduce (2010, methodology): “There already exist many published work optimizing PageRank algorithm, like some of them accelerate computation by exploring the block structure of hyperlinks[31, 32].”
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 Random Walk with Restart and Its Applications (2006, methodology): “Existing fast solution for RWR include Hub-vector decomposition based [16]; block structure based [18] [25]; fingerprint based [9], and so on.”
Adaptive methods for the computation of PageRank (2004, unknown): “Kamvar et al. use extrapolation techniques in [13] and aggregation/disaggregation techniques in [ 12 ] to significantly speed up convergence.”
The link prediction problem for social networks (2003, unknown): “We now re-compute score ( x, y ) for all pairs (cid:1) x, y (cid:2) on this subgraph.”
Fast Incremental and Personalized PageRank (2010, methodology): “Kamvar et al. [21] use the host-induced block structure of the web link graph to speed up computation and updating of PageRank and also provide personalization.”