pith. sign in

arxiv: 1705.09927 · v2 · pith:TVC5CYVUnew · submitted 2017-05-28 · 💻 cs.DC · cs.SY

Fully distributed PageRank computation with exponential convergence

Liang Dai , Nikolaos M. Freris This is my paper
classification 💻 cs.DC cs.SY
keywords distributedfullyconvergenceexponentialpagerankalgorithmcomputationcomputing
0
0 comments X
read the original abstract

This work studies a fully distributed algorithm for computing the PageRank vector, which is inspired by the Matching Pursuit and features: 1) a fully distributed implementation 2) convergence in expectation with exponential rate 3) low storage requirement (two scalar values per page). Illustrative experiments are conducted to verify the findings.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Efficient PageRank Computation via Distributed Algorithms with Web Clustering

    eess.SY 2019-07 unverdicted novelty 5.0

    Introduces gossip-based and clustering-enhanced distributed algorithms for PageRank computation with claimed exponential convergence rates, shown via numerical examples on real web data.