This paper proposes a simple 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 dynamic graphs from the latent representations. The informative prior distributions in the latent spaces are learned from observed data as empirical Bayes, and the expressive power of a generative model is exploited to assist change point detection. Specifically, the model parameters are learned via maximum approximate likelihood, with a Group Fused Lasso regularization. The optimization problem is then solved via Alternating Direction Method of Multipliers (ADMM), and Langevin Dynamics are recruited for posterior inference. Experiments in simulated and real data demonstrate the ability of the generative model in supporting change point detection with good performance.

翻译：本文提出一种简单的生成模型，用于检测图时间序列中的变点。该框架包含可学习的图低维表示先验分布，以及能够从隐表示生成动态图的解码器。隐空间中的信息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变点检测。具体而言，模型参数通过最大近似似然估计进行学习，并引入群体融合Lasso正则化。优化问题采用交替方向乘子法求解，后验推断则通过朗之万动力学实现。在模拟数据和真实数据上的实验表明，该生成模型能有效支持变点检测，且性能表现优异。