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Improving Random Walk Estimation Accuracy with Uniform Restarts

Abstract : This work proposes and studies the properties of a hybrid sampling scheme that mixes independent uniform node sampling and random walk (RW)-based crawling. We show that our sampling method combines the strengths of both uniform and RW sampling while minimizing their drawbacks. In particular, our method increases the spectral gap of the random walk, and hence, accelerates convergence to the stationary distribution. The proposed method resembles PageRank but unlike PageRank preserves time-reversibility. Applying our hybrid RW to the problem of estimating degree distributions of graphs shows promising results.
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Contributor : Konstantin Avrachenkov Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2010 - 12:45:38 AM
Last modification on : Friday, November 18, 2022 - 9:27:11 AM
Long-term archiving on: : Thursday, June 30, 2011 - 1:30:09 PM


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  • HAL Id : inria-00520350, version 1



Konstantin Avrachenkov, Bruno Ribeiro, Don Towsley. Improving Random Walk Estimation Accuracy with Uniform Restarts. [Research Report] RR-7394, INRIA. 2010. ⟨inria-00520350⟩



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