基于仅解码器潜在空间模型的动态图变化点检测
Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model
AI总结：
针对图时间序列的无监督变化点检测问题，提出含可学习先验分布和组融合Lasso正则化的仅解码器潜在空间模型，通过交替方向乘子法和朗之万动力学求解，模拟与真实数据实验验证其有效性。
摘要：
本手稿研究了使用仅解码器潜在空间模型在图时间序列中进行无监督变化点检测的问题。所提出的框架包含低维图表征的可学习先验分布，以及连接观测图和潜在表征的解码器。潜在空间的先验分布通过经验贝叶斯从观测数据中学习，以辅助变化点检测。具体而言，模型参数通过最大近似似然估计，对先验参数施加组融合Lasso正则化。增广拉格朗日通过交替方向乘子法求解，朗之万动力学用于后验推断。模拟研究表明该潜在空间模型在支持变化点检测方面表现良好，真实数据实验得到的变化点与重大事件一致。
英文摘要：
This manuscript studies the unsupervised change point detection problem in time series of graphs using a decoder-only latent space model. The proposed framework consists of learnable prior distributions for low-dimensional graph representations and of a decoder that bridges the observed graphs and latent representations. The prior distributions of the latent spaces are learned from the observed data as empirical Bayes to assist change point detection. Specifically, the model parameters are estimated via maximum approximate likelihood, with a Group Fused Lasso regularization imposed on the prior parameters. The augmented Lagrangian is solved via Alternating Direction Method of Multipliers, and Langevin Dynamics are recruited for posterior inference. Simulation studies show good performance of the latent space model in supporting change point detection and real data experiments yield change points that align with significant events.
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