Generative Model for Change Point Detection in Dynamic Graphs
Abstract: 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 prior distributions of the latent spaces are learned from the observed data as empirical Bayes and generative model is employed 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 and Langevin Dynamics are recruited for posterior inference. Simulation studies show good performance of the generative model in supporting change point detection, and real data experiments yield change points that align with significant events.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Sinead_Williamson1
Submission Number: 2904
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