Improving Random Walk Estimation Accuracy with Uniform Restarts

Konstantin Avrachenkov (1) , Bruno Ribeiro (2) , Don Towsley (2)

1 MAESTRO - Models for the performance analysis and the control of networks
2 Department of Computer Science [Amherst]

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.

Mots clés: Sampling; Random Walk; Spectral Gap; PageRank; Online Social Network

Domaines: Réseaux et télécommunications [cs.NI]

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