  Numerical and experimental turbulence simulations are nowadays reaching the
size of the so-called big data, thus requiring refined investigative tools for
appropriate statistical analyses and data mining. We present a new approach
based on the complex network theory, offering a powerful framework to explore
complex systems with a huge number of interacting elements. Although interest
on complex networks has been increasing in the last years, few recent studies
have been applied to turbulence. We propose an investigation starting from a
two-point correlation for the kinetic energy of a forced isotropic field
numerically solved. Among all the metrics analyzed, the degree centrality is
the most significant, suggesting the formation of spatial patterns which
coherently move with similar vorticity over the large eddy turnover time scale.
Pattern size can be quantified through a newly-introduced parameter (i.e.,
average physical distance) and varies from small to intermediate scales. The
network analysis allows a systematic identification of different spatial
regions, providing new insights into the spatial characterization of turbulent
flows. Based on present findings, the application to highly inhomogeneous flows
seems promising and deserves additional future investigation.
