  Efficiently mapping baryonic properties onto dark matter is a major challenge
in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical
simulations have made impressive advances in reproducing galaxy observables
across cosmologically significant volumes, these methods still require
significant computation times, representing a barrier to many applications.
Graph Neural Networks (GNNs) have recently proven to be the natural choice for
learning physical relations. Among the most inherently graph-like structures
found in astrophysics are the dark matter merger trees that encode the
evolution of dark matter halos. In this paper we introduce a new, graph-based
emulator framework, $\texttt{Mangrove}$, and show that it emulates the galactic
stellar mass, cold gas mass and metallicity, instantaneous and time-averaged
star formation rate, and black hole mass -- as predicted by a SAM -- with root
mean squared error up to two times lower than other methods across a $(75
Mpc/h)^3$ simulation box in 40 seconds, 4 orders of magnitude faster than the
SAM. We show that $\texttt{Mangrove}$ allows for quantification of the
dependence of galaxy properties on merger history. We compare our results to
the current state of the art in the field and show significant improvements for
all target properties. $\texttt{Mangrove}$ is publicly available.
