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.

翻译：本文提出一种生成模型，用于检测图时间序列中的变化点。所提出的框架包含可学习的低维图表示先验分布，以及一个能从潜在表示生成图的解码器。潜在空间中信息丰富的先验分布通过经验贝叶斯方法从观测数据中学习，并利用生成模型的表达能力辅助多重变化点检测。具体而言，模型参数通过最大近似似然进行学习，并对先验参数施加组套索融合正则化。该优化问题通过交替方向乘子法（ADMM）求解，并采用朗之万动力学进行后验推断。在模拟数据和真实数据上的实验表明，该生成模型能够有效支持变化点检测并取得良好性能。