Introduces gossip-based and clustering-enhanced distributed algorithms for PageRank computation with claimed exponential convergence rates, shown via numerical examples on real web data.
Fully distributed PageRank computation with exponential convergence
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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.
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eess.SY 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Efficient PageRank Computation via Distributed Algorithms with Web Clustering
Introduces gossip-based and clustering-enhanced distributed algorithms for PageRank computation with claimed exponential convergence rates, shown via numerical examples on real web data.