  Complex systems such as ecological communities and neuron networks are
essential parts of our everyday lives. These systems are composed of units
which interact through intricate networks. The ability to predict sudden
changes in the dynamics of these networks, known as critical transitions, from
data is important to avert disastrous consequences of major disruptions.
Predicting such changes is a major challenge as it requires forecasting the
behaviour for parameter ranges for which no data on the system is available. We
address this issue for networks with weak individual interactions and chaotic
local dynamics. We do this by building a model network, termed an {\em
effective network}, consisting of the underlying local dynamics and a
statistical description of their interactions. We show that behaviour of such
networks can be decomposed in terms of an emergent deterministic component and
a {\em fluctuation} term. Traditionally, such fluctuations are filtered out.
However, as we show, they are key to accessing the interaction structure.
