Sequential detection of multiple change points in networks: a graphical model approach
This work addresses the problem of monitoring dynamic networks for change points, which is incremental as it builds on graphical models to improve sequential detection methods.
The authors tackled the problem of sequentially detecting multiple change points in networks by proposing a probabilistic formulation and a class of detection rules, achieving asymptotic optimality in expected detection delay time and demonstrating effectiveness through simulations.
We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection delay time. Drawing from graphical model formalism, the sequential detection rules can be implemented by a computationally efficient message-passing protocol which may scale up linearly in network size and in waiting time. The effectiveness of our inference algorithm is demonstrated by simulations.