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Variance reduction in large graph sampling
2014
Information Processing & Management
The norm of practice in estimating graph properties is to use uniform random node (RN) samples whenever possible. Many graphs are large and scale-free, inducing large degree variance and estimator variance. This paper shows that random edge (RE) sampling and the corresponding harmonic mean estimator for average degree can reduce the estimation variance significantly. First, we demonstrate that the degree variance, and consequently the variance of the RN estimator, can grow almost linearly with
doi:10.1016/j.ipm.2014.02.003
fatcat:4jvupneuinekld2u2wr67rerzq