  Adaptively monitoring the states of nodes in a large complex network is of
interest in domains such as national security, public health, and energy grid
management. Here, we present an information theoretic adaptive tracking and
sampling framework that recursively selects measurements using the feedback
from performing inference on a dynamic Bayesian Network. We also present
conditions for the existence of a network specific, observation dependent,
phase transition in the updated posterior of hidden node states resulting from
actively monitoring the network. Since traditional epidemic thresholds are
derived using observation independent Markov chains, the threshold of the
posterior should more accurately model the true phase transition of a network.
The adaptive tracking framework and epidemic threshold should provide insight
into modeling the dynamic response of the updated posterior to active
intervention and control policies while monitoring modern complex networks.
