• Corpus ID: 244129842

Non-separable Spatio-temporal Graph Kernels via SPDEs

  title={Non-separable Spatio-temporal Graph Kernels via SPDEs},
  author={Alexander P. Nikitin and S. T. John and A. Solin and Samuel Kaski},
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling has held back their use in graph problems. We leverage an explicit link between stochastic partial di ↵ erential equations (SPDEs) and GPs on graphs, introduce a framework for deriving graph kernels via SPDEs, and derive non-separable spatio-temporal graph kernels that capture interaction across space and time. We… 

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