VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks

Change point detection in dynamic networks aims to detect the points of sudden change or abnormal events within the network. It has garnered substantial interest from researchers due to its potential to enhance the stability and reliability of real-world networks. Most change point detection methods are based on statistical characteristics and phased training, and some methods are required to set the percent of change points. Meanwhile, existing methods for change point detection suffer from two limitations. On one hand, they struggle to extract snapshot features that are crucial for accurate change point detection, thereby limiting their overall effectiveness. On the other hand, they are typically tailored for specific network types and lack the versatility to adapt to networks of varying scales. To solve these issues, we propose a novel unified end-to-end framework called Variational Graph Gaussian Mixture model (VGGM) for change point detection in dynamic networks. Specifically, VGGM combines Variational Graph Auto-Encoder (VGAE) and Gaussian Mixture Model (GMM) through joint training, incorporating a Mixture-of-Gaussians prior to model dynamic networks. This approach yields highly effective snapshot embeddings via VGAE and a dedicated readout function, while automating change point detection through GMM. The experimental results, conducted on both real-world and synthetic datasets, clearly demonstrate the superiority of our model in comparison to the current state-of-the-art methods for change point detection.