enough information to forecast and anticipate the network behaviour, even in situations where the parameters of the system change into ranges that have not been previously encountered.

[**Complex networks of nonlinear systems.**]{} We consider networks with $N$ nodes with chaotic isolated dynamics and pairwise interactions satisfying some additional assumptions described below. The network is described by its adjacency matrix ${\boldsymbol}A$, whose entry $A_{ij}$ equals $1$ if node $i$ receives a connection from $j$ and equals $0$ otherwise. We assume that the time evolution of the state ${\boldsymbol}x_i(t)$ of node $i$ at time $t$ is expressed as $$\begin{aligned}
\bm{x}_i(t+1)&=&\bm{F}_i (\bm{x}_i(t))+\alpha \sum_{j=1}^{N} A_{ij} \bm{H}(\bm{x}_i(t),\bm{x}_j(t)). 
\label{eq1}\end{aligned}$$ When performing reconstruction from data, the isolated local dynamics $\bm{F}_i\colon M\to M$, the coupling function $\bm{H}$, the coupling parameter $\alpha$ (that is small), the adjacency matrix ${\boldsymbol}A$, and even the dimension $k$ of the space $M$, are assumed to be [*unknown*]{}. This class of equations can be applied to modelling many important complex systems found in neuroscience (e.g. coupled equations in neuron networks) [@izhikevich2007], engineering (e.g. smart grids) [@yadav2017; @dorfler2013], material sciences (e.g. superconductors) [@watanabe1994], and biology (e.g. cardiac pacemaker cells) [@winfree2001], among other systems.

Our three assumptions are: $(a)$ the local dynamics are close to some 