Improving Random Walk Estimation Accuracy with Uniform Restarts

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 Page Rank but unlike Page Rank preserves time-reversibility. Applying our hybrid RW to the problem of estimating degree distributions of graphs shows promising results.