  KM3NeT, a neutrino telescope currently under construction in the
Mediterranean Sea, consists of a network of large-volume Cherenkov detectors.
Its two different sites, ORCA and ARCA, are optimised for few GeV and TeV-PeV
neutrino energies, respectively. This allows for studying a wide range of
physics topics spanning from the determination of the neutrino mass hierarchy
to the detection of neutrinos from astrophysical sources. Deep Learning
techniques provide promising methods to analyse the signatures induced by
charged particles traversing the detector. This document will cover a Deep
Learning based approach using Graph Convolutional Networks to classify and
reconstruct events in both the ORCA and ARCA detector. Performance studies on
simulations as well as applications to real data will be presented, together
with comparisons to classical approaches.
