This chapter presents statistical and algorithmic approaches to discovering groups of actors that hide their communications within the myriad of background communications in a large communication network. Our approach to discovering hidden groups is based on the observation that a pattern of communications exhibited by actors in a social group pursuing a common objective is different from that of a randomly selected set of actors. We distinguish two types of hidden groups: temporal, which exhibits repeated communication patterns; and spatialwhich exhibits correlations within a snapshot of communications aggregated over some time interval. We present models and algorithms, together with experiments showing the performance of our algorithms on simulated and real data inputs.