The growth of the Internet and the emergence of online social websites bring up the development of massive networks which are large in scale, complex in structure, and dynamical in time. Exploring latent structure underlying a network is the fundamental solution to understand and analyze the network. Probabilistic models become effective tools in diverse areas of structure exploratory due to their flexibility in modeling, interpretability and the sound theoretical framework, however they incur computational bottlenecks. Recently, several approaches based on probabilistic models have been developed to explore structure in massive networks, which aim to solve the computational problems from three aspects: representations of a network, assumptions of the structure and methods of parameter estimation. This study classifies existing approaches as two categories by the methods of parameter estimation: approaches based on stochastic variational inference and online EM approaches, and analyzes in detail their designing incentives, principles, pros and cons. The properties and performance of classical models are compared and analyzed qualitatively and quantitatively, and as a result the principles are provided to develop approaches of structure detection in massive networks. Finally, the core problems of structure exploratory in massive networks are summarized based on probabilistic models and the development trend of this area is projected.