  We present a novel model Graph Neural Stochastic Differential Equations
(Graph Neural SDEs). This technique enhances the Graph Neural Ordinary
Differential Equations (Graph Neural ODEs) by embedding randomness into data
representation using Brownian motion. This inclusion allows for the assessment
of prediction uncertainty, a crucial aspect frequently missed in current
models. In our framework, we spotlight the \textit{Latent Graph Neural SDE}
variant, demonstrating its effectiveness. Through empirical studies, we find
that Latent Graph Neural SDEs surpass conventional models like Graph
Convolutional Networks and Graph Neural ODEs, especially in confidence
prediction, making them superior in handling out-of-distribution detection
across both static and spatio-temporal contexts.
