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Graph Neural Stochastic Differential Equations
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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.
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Cited by 1 Pith paper
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Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda
Combining causal learning with Neurochaos Learning inside graph neural networks is proposed as a research direction, with open questions but no experimental validation.
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