  Link prediction on dynamic graphs is an important task in graph mining.
Existing approaches based on dynamic graph neural networks (DGNNs) typically
require a significant amount of historical data (interactions over time), which
is not always available in practice. The missing links over time, which is a
common phenomenon in graph data, further aggravates the issue and thus creates
extremely sparse and dynamic graphs. To address this problem, we propose a
novel method based on the neural process, called Graph Sequential Neural ODE
Process (GSNOP). Specifically, GSNOP combines the advantage of the neural
process and neural ordinary differential equation that models the link
prediction on dynamic graphs as a dynamic-changing stochastic process. By
defining a distribution over functions, GSNOP introduces the uncertainty into
the predictions, making it generalize to more situations instead of overfitting
to the sparse data. GSNOP is also agnostic to model structures that can be
integrated with any DGNN to consider the chronological and geometrical
information for link prediction. Extensive experiments on three dynamic graph
datasets show that GSNOP can significantly improve the performance of existing
DGNNs and outperform other neural process variants.
