Sequential detection of multiple change points in networks: a graphical model approach

TL;DR

This paper introduces a probabilistic, scalable method for sequentially detecting multiple change points in networks using graphical models, with proven asymptotic optimality and demonstrated effectiveness through simulations.

Contribution

It presents a novel probabilistic framework and message-passing algorithm for efficient, asymptotically optimal detection of multiple change points in network data.

Findings

Algorithm scales linearly with network size

Proven asymptotic optimality of detection rules

Effective detection demonstrated via simulations

Abstract

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.