Respondent-Driven Sampling for Characterizing Unstructured Overlays

This paper presents Respondent-Driven Sampling (RDS) as a promising technique to derive unbiased estimates of node properties in unstructured overlay networks such as Gnutella. Using RDS and a previously proposed technique, namely Metropolized Random Walk (MRW) sampling, we examine the efficiency of estimating node properties in unstructured overlays and identify some of the key factors that determine the accuracy of sampling techniques. We evaluate the RDS and MRW techniques using simulation over a wide range of static and dynamic graphs as well as experiments over a widely deployed Gnutella network. Our study sheds light on how the connectivity structure among nodes and its dynamics affect the accuracy and efficiency of the two sampling techniques. Both techniques exhibit a rather similar performance over a wide range of scenarios. However, RDS significantly outperforms MRW when the overlay structure exhibits a combination of highly skewed node degrees and highly skewed (local) clustering coefficients.