  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.
