  Many kinds of data can be represented as a network or graph. It is crucial to
infer the latent structure underlying such a network and to predict unobserved
links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a
promising model for network data. Latent variables and unknown parameters in
MMSB have been estimated through Bayesian inference with the entire network;
however, it is important to estimate them online for evolving networks. In this
paper, we first develop online inference methods for MMSB through sequential
Monte Carlo methods, also known as particle filters. We then extend them for
time-evolving networks, taking into account the temporal dependency of the
network structure. We demonstrate through experiments that the time-dependent
particle filter outperformed several baselines in terms of prediction
performance in an online condition.
