  Modern graph representation learning works mostly under the assumption of
dealing with regularly sampled temporal graph snapshots, which is far from
realistic, e.g., social networks and physical systems are characterized by
continuous dynamics and sporadic observations. To address this limitation, we
introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework,
which learns both the temporal and spatial dynamics from graph streams where
the intervals between observations are not regularly spaced. We empirically
validate the proposed approach on several graph benchmarks, showing that TG-ODE
can achieve state-of-the-art performance in irregular graph stream tasks.
