  A fundamental premise of statistical physics is that the particles in a
physical system are interchangeable, and hence the state of each specific
component is representative of the system as a whole. This assumption breaks
down for complex networks, in which nodes may be extremely diverse, and no
single component can truly represent the state of the entire system. It seems,
therefore, that to observe the dynamics of social, biological or technological
networks, one must extract the dynamic states of a large number of nodes -- a
task that is often practically prohibitive. To overcome this challenge, we use
machine learning techniques to detect the network's sentinel nodes, a set of
network components whose combined states can help approximate the average
dynamics of the entire network. The method allows us to assess the state of a
large complex system by tracking just a small number of carefully selected
nodes. The resulting sentinel node set offers a natural probe by which to
practically observe complex network dynamics.
