Statistical network analysis typically deals with inference concerning various parameters of an observed network. In several applications, especially those from social sciences, behavioral information concerning groups of subjects are observed. In such data sets, even though a network structure is present it is not typically observed. These are referred to as implicit networks. We describe a model-based framework to uncover the implicit network structure and address related inferential questions. Theoretical properties such as model idenfiability and estimation consistency will be discussed.