  We present a new approach for the identification of ultra-high energy cosmic
rays from sources using dynamic graph convolutional neural networks. These
networks are designed to handle sparsely arranged objects and to exploit their
short- and long-range correlations. Our method searches for patterns in the
arrival directions of cosmic rays, which are expected to result from coherent
deflections in cosmic magnetic fields. The network discriminates astrophysical
scenarios with source signatures from those with only isotropically distributed
cosmic rays and allows for the identification of cosmic rays that belong to a
deflection pattern. We use simulated astrophysical scenarios where the source
density is the only free parameter to show how density limits can be derived.
We apply this method to a public data set from the AGASA Observatory.
