Do neutrinos, the elementary particles, have something in common with fake news on social media? The peculiar and positive answer comes from a group of researchers at USI Institute of Computational Science, and it shows how both their behaviour can be represented using the same data structure. Such structure is based on a non-Euclidean geometry and can be studied through a new class of algorithms: the Graph Convolutional Neural Networks (GCNN). Such algorithms are highly complex mathematical models, and the research work carried out by Federico Monti, member of Prof. Michael Bronstein group, earned him the award for the best scientific contribution assigned by ICMLA, the most important international conference in the field. Monti, in collaboration with other colleagues from New York University, Berkeley and Imperial College, had the opportunity to collaborate with the Lawrence Berkley National Laboratory on data acquired by the IceCube Neutrino Observatory at the South Pole.