  We present the study of a fuzzy clustering algorithm for the Belle II
electromagnetic calorimeter using Graph Neural Networks. We use a realistic
detector simulation including simulated beam backgrounds and focus on the
reconstruction of both isolated and overlapping photons. We find significant
improvements of the energy resolution compared to the currently used
reconstruction algorithm for both isolated and overlapping photons of more than
30% for photons with energies E < 0.5 GeV and high levels of beam backgrounds.
Overall, the GNN reconstruction improves the resolution and reduces the tails
of the reconstructed energy distribution and therefore is a promising option
for the upcoming high luminosity running of Belle II.
