1. A method to find dynamic social networks, comprising: a. applying a computer to a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, b. determining Gibbs sampling as a conditional probability of a community assignment of each node conditioned on the community assignments of other nodes as Pr(z i (t) |Z T,{i,t} − , W T ), via Pr(Z T |W T ), where Z T,{i,t} − are community assignments of all nodes at all time steps except node i at time step t, and c. inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.