Multiple Systems Estimation (MSE) methods encompass a range of models designed for estimating the population size of a closed population using several partial lists. These approaches seek to estimate the probability of individuals appearing on various combinations of lists. We present and explore the most commonly used models, assessing their advantages and limitations through various simulated scenarios. We find some practical conditions in which all approaches perform poorly. Ultimately, we apply these models to approximate the total fatalities in the Kosovo conflict of 1999. Respondent-Driven Sampling (RDS) is a widely used method for recruiting samples from hidden or hard-to-reach populations through social connections among members. This chapter enhances traditional RDS, which captures connections via coupon-based recruitment, leading to a set of tree-structured networks, by introducing a novel augmentation involving the distribution of tokens. This augmentation allows for the exploration of otherwise missed cross-ties, enhancing our understanding of the clusters within these populations. Firstly, we adapt a variant of the logistic regression model from Ward et al. (2009), utilizing an Expectation-Maximization (EM) algorithm to handle positive-unlabeled data where observed ties are labeled, and non-observed ties and non-ties remain unlabeled. Secondly, we employ a conditional Exponential Random Graph Model (ERGM) for partially observed networks (Handcock and Gile, 2010) that approximates the maximum likelihood function by exploring the set of possible networks consistent with the observed ties. For both methods, we focus on modeling token ties by estimating the overall density of the subgraph of sampled nodes and treating it as observed data, which enables us to make likelihood-based inference without including the sampling design parameter. Additionally, we get around the need to model complex missingness structure of RDS by conditioning on RDS ties. The performance of our estimators of the subgraph of sampled nodes is assessed under various simulation settings.