  Dynamical systems in which local interactions among agents give rise to
complex emerging phenomena are ubiquitous in nature and society. This work
explores the problem of inferring the unknown interaction structure
(represented as a graph) of such a system from measurements of its constituent
agents or individual components (represented as nodes). We consider a setting
where the underlying dynamical model is unknown and where different
measurements (i.e., snapshots) may be independent (e.g., may stem from
different experiments). We propose GINA (Graph Inference Network Architecture),
a graph neural network (GNN) to simultaneously learn the latent interaction
graph and, conditioned on the interaction graph, the prediction of a node's
observable state based on adjacent vertices. GINA is based on the hypothesis
that the ground truth interaction graph -- among all other potential graphs --
allows to predict the state of a node, given the states of its neighbors, with
the highest accuracy. We test this hypothesis and demonstrate GINA's
effectiveness on a wide range of interaction graphs and dynamical processes.
