  Respondent-driven sampling (RDS) is a link-tracing sampling method that is
especially suitable for sampling hidden populations. RDS combines an efficient
snowball-type sampling scheme with inferential procedures that yield unbiased
population estimates under some assumptions about the sampling procedure and
population structure. Several seed individuals are typically used to initiate
RDS recruitment. However, standard RDS estimation theory assume that all
sampled individuals originate from only one seed. We present an estimator,
based on a random walk with teleportation, which accounts for the multiple seed
structure of RDS. The new estimator can also be used on populations with
disconnected social networks. We numerically evaluate our estimator by
simulations on artificial and real networks. Our estimator outperforms previous
estimators, especially when the proportion of seeds in the sample is large. We
recommend our new estimator to be used in RDS studies, in particular when the
number of seeds is large or the social network of the population is
disconnected.
