The four cosmic tidal web elements from the $\beta$-skeleton
  Precise cosmic web classification of observed galaxies in massive
spectroscopic surveys can be either highly uncertain or computationally
expensive. As an alternative, we explore a fast Machine Learning-based approach
to infer the underlying dark matter tidal cosmic web environment of a galaxy
distribution from its $\beta$-skeleton graph. We develop and test our
methodology using the cosmological magnetohydrodynamic simulation Illustris-TNG
at $z=0$. We explore three different tree-based machine-learning algorithms to
find that a random forest classifier can best use graph-based features to
classify a galaxy as belonging to a peak, filament or sheet as defined by the
T-Web classification algorithm. The best match between the galaxies and the
dark matter T-Web corresponds to a density field smoothed over scales of $2$
Mpc, a threshold over the eigenvalues of the dimensionless tidal tensor of
$\lambda_{\rm{th}}=0.0$ and galaxy number densities around $8\times 10^{-3}$
Mpc$^{-3}$. This methodology results on a weighted F1 score of 0.728 and a
global accuracy of 74\%. More extensive tests that take into account lightcone
effects and redshift space distortions (RSD) are left for future work. We make
one of our highest ranking random forest models available on a public
repository for future reference and reuse.
