  This paper proposes a generative model to detect change points in time series
of graphs. The proposed framework consists of learnable prior distributions for
low-dimensional graph representations and of a decoder that can generate graphs
from the latent representations. The informative prior distributions in the
latent spaces are learned from the observed data as empirical Bayes, and the
expressive power of generative model is exploited to assist multiple change
point detection. Specifically, the model parameters are learned via maximum
approximate likelihood, with a Group Fused Lasso regularization on the prior
parameters. The optimization problem is then solved via Alternating Direction
Method of Multipliers (ADMM), and Langevin Dynamics are recruited for posterior
inference. Experiments in both simulated and real data demonstrate the ability
of the generative model in supporting change point detection with good
performance.
