Respondent-driven sampling (RDS) is very useful in collecting data from individuals in hidden populations, where a sampling frame does not exist. It starts with researchers choosing initial respondents from a group which may be involved in taboo or illegal activities, after which they recruit other peers who belong to the same group. Analysis results in unbiased estimates of population proportions though with strong assumptions about the underlying social network and RDS recruitment process. These assumptions bear little resemblance to reality, and thus compromise the estimation of any means, population proportions or variances inferred from studies. The topology of the contact network, denoted by the number of links each person has, provides insight into the processes of infectious disease spread. The overall objective of the thesis is to identify the topology of an injection drug use network, and critically review the methods developed to produce estimates. The topology of people who inject drugs (PWID) collected by RDS in Ottawa, 2006 was compared with a Poisson distribution, an exponential distribution, a power-law distribution, and a lognormal distribution. The contact distribution was then evaluated against a small-world network characterized by high clustering and low average distances between individuals. Last a systematic review of the methods used to produce RDS mean and variance estimates was conducted. The Poisson distribution, a type of random distribution, was not an appropriate fit for PWID network. However, the PWID network can be classified as a small world network organised with many connections and short distances between people. Prevention of transmission in such networks should be focussed on the most active people (clustered individuals and hubs) as intervention with any others is less effective. The systematic review contained 32 articles which included the development and evaluation of 12 RDS mean and 6 variance estimators. Overall, the majority of estimators perform roughly the same, with the exception of RDSIEGO which outperformed the 6 other RDS mean estimators. The Tree bootstrap variance estimate does not rely on modelling RDS as a first order Markov (FOM) process, which seems to be the main limitation of the other existing estimators. The lack of FOM as an assumption and the flexible application of this variance estimator to any RDS point estimate make the Tree bootstrapping estimator a more efficient choice.