  There has been an increasing interest in modeling continuous-time dynamics of
temporal graph data. Previous methods encode time-evolving relational
information into a low-dimensional representation by specifying discrete layers
of neural networks, while real-world dynamic graphs often vary continuously
over time. Hence, we propose Continuous Temporal Graph Networks (CTGNs) to
capture the continuous dynamics of temporal graph data. We use both the link
starting timestamps and link duration as evolving information to model the
continuous dynamics of nodes. The key idea is to use neural ordinary
differential equations (ODE) to characterize the continuous dynamics of node
representations over dynamic graphs. We parameterize ordinary differential
equations using a novel graph neural network. The existing dynamic graph
networks can be considered as a specific discretization of CTGNs. Experiment
results on both transductive and inductive tasks demonstrate the effectiveness
of our proposed approach over competitive baselines.
