Gaussian Processes on Hypergraphs

Understand

We derive a Matern Gaussian process (GP) on the vertices of a hypergraph.

• This enables estimation of regression models of observed or latent values associated with the vertices, in which the correlation and uncertainty estimates are informed by the hypergraph structure.
• We further present a framework for embedding the vertices of a hypergraph into a latent space using the hypergraph GP.
• Finally, we provide a scheme for identifying a small number of representative inducing vertices that enables scalable inference through sparse GPs.