  We propose an analytical technique to study large fluctuations and switching
from internal noise in complex networks. Using order-disorder kinetics as a
generic example, we construct and analyze the most probable, or optimal path of
fluctuations from one ordered state to another in real and synthetic networks.
The method allows us to compute the distribution of large fluctuations and the
time scale associated with switching between ordered states for networks
consistent with mean-field assumptions. In general, we quantify how network
heterogeneity influences the scaling patterns and probabilities of
fluctuations. For instance, we find that the probability of a large fluctuation
near an order-disorder transition decreases exponentially with the
participation ratio of a network's principle eigenvector -- measuring how many
nodes effectively contribute to an ordered state. Finally, the proposed theory
is used to answer how and where a network should be targeted in order to
optimize the time needed to observe a switch.
