Theoretical analysis of structured learning methods has focused primarily on domains where the data consist of {\em independent} (albeit structured) examples. Although the statistical relational learning (SRL) community has recently developed many classification methods for graph and network domains, much of this work has focused on modeling domains where there is a {\em single} network for learning. For example, we could learn a model to predict the political views of users in an online social network, based on the friendship relationships among users. In this example, the data would be drawn from a single large network (e.g., Facebook) and increasing the data size would correspond to acquiring a larger graph. Although SRL methods can successfully improve classification in these types of domains, there has been little theoretical analysis of addressing the issue of single network domains. In particular, the asymptotic properties of estimation are not clear if the size of the model grows with the size of the network. In this work, we focus on outlining the conditions under which learning from a single network will be asymptotically consistent and normal. Moreover, we compare the properties of maximum likelihood estimation (MLE) with that of generalized maximum pseudolikelihood estimation (MPLE) and use the resulting understanding to propose novel MPLE estimators for single network domains. We include empirical analysis on both synthetic and real network data to illustrate the findings.