Exploring the statistical properties and hidden characteristics of network entities, and the stochastic processes behind temporal evolution of network topologies, are essential for computational knowledge discovery and prediction based on network data from biology, social sciences and various other fields. In this talk, I first discuss a hierarchical Bayesian framework that combines the mixed membership model and the stochastic blockmodel for inferring latent multi-facet roles of nodes in networks, and for estimating stochastic relationships (i.e., cooperativeness or
antagonisms) between roles. Then I discuss a new formalism for modeling network evolution over time based on temporal exponential random graphs, and a MCMC algorithm for posterior inference of the latent time-specific networks. The proposed methodology makes it possible to reverse-engineer the latent sequence of temporally rewiring networks given longitudinal measurements of node attributes, such as intensities of gene expressions or social metrics of actors, even when a single snapshot of such measurement resulted from each (time-specific) network is available.