  The IceCube Neutrino Observatory is a multi-component detector embedded deep
within the South-Pole Ice. This proceeding will discuss an analysis from an
integrated operation of IceCube and its surface array, IceTop, to estimate
cosmic-ray composition. The work will describe a novel graph neural network
based approach for estimating the mass of primary cosmic rays, that takes
advantage of signal-footprint information and reconstructed cosmic-ray air
shower parameters. In addition, the work will also introduce new
composition-sensitive parameters for improving the estimation of cosmic-ray
composition, with the potential of improving our understanding of the
high-energy muon content in cosmic-ray air showers.
