Before revision:  To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel which embeds an actor into a latent space and track its mixed membership in the latent space across time . We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
After revision:  To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel for static networks, and the state-space model for tracking object trajectory. It overcomes a major limitation of many current network inference techniques, which assume that each actor plays a unique and invariant role that accounts for all its interactions with other actors; instead, our method models the role of each actor as a time-evolving mixed membership vector that allows actors to behave differently over time and carry out different roles/functions when interacting with different peers, which is closer to reality. We present an efficient algorithm for approximate inference and learning using our model; and we applied our model to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.