Despite much progress has been made, several mechanisms about turbulence dynamics are still
unclear. We propose an innovative approach based on complex networks theory, which combines
elements from graph theory and statistical physics, providing a powerful framework to investigate
complex systems.The network is built on a forced isotropic turbulent field, by evaluating the
temporal correlation of the kinetic energy for pairs of nodes within the Taylor microscale, λ. Among
all the parameters analyzed, the degree centrality, k, is one of the most meaningful, representing
how a node is linked to the others. We observe 3D patterns of high k values, which can be
interpreted as regions of spatial coherence. The turbulent network exhibits typical behaviors of
real and spatial networks (scale-free property). Similarly to other physical systems where complex
networks successfully apply, our approach can give new insights for the spatial characterization of
turbulence
