  Water-(Ice-) Cherenkov neutrino telescopes have played a pivotal role in the
search and discovery of high-energy astrophysical neutrinos. Experimental
collaborations are developing and constructing next-generation neutrino
telescopes with improved optical modules (OMs) and larger geometrical volumes
to increase their efficiency in the multi-TeV energy range and extend their
reach to EeV energies. Although most existing telescopes share similar OM
layouts, more layout options should be explored for next-generation detectors
to maximize discovery capability. In this work, we study a set of layouts at
different geometrical volumes and evaluate the signal event selection
efficiency and reconstruction fidelity under both an only trigger-level linear
regression algorithm and an offline Graph Neural Network (GNN) reconstruction.
Our methodology and findings serve as first steps toward an optimized, global
network of neutrino telescopes.
