in the number of contacts. In RDS, the structure of the social contacts thus denes the sampling process and aects its coverage, for instance by constraining the sampling within a sub-region of the network. In this paper we study the bias induced by network structures such as social triangles, community structure, and heterogeneities in the number of contacts, in the recruitment trees and in the RDS estimator. We simulate dierent scenarios of network structures and response-rates to study the potential biases one may expect in real settings. We nd that the prevalence of the estimated variable is associated with the size of the network community to which the individual belongs. Furthermore, we observe that low-degree nodes may be under-sampled in certain situations if the sample and the network are of similar size. Finally, we also show that low response-rates lead to reasonably accurate average estimates of the prevalence but generate relatively large biases.
['Luis E. C. Rocha', 'Anna Ekéus Thorson', 'Renaud Lambiotte', 'Fredrik Liljeros']
Respondent-driven sampling bias induced by clustering and community structure in social networks