mlsanityData to 14 Oct 2026

Multi-Output Gaussian Processes for Graph-Structured Data

arXiv 2505.16755 ↗First posted 22 May 2025cs.LGcited by 0

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Abstract (arXiv)

Graph-structured data is a type of data to be obtained associated with a graph structure where vertices and edges describe some kind of data correlation. This paper proposes a regression method on graph-structured data, which is based on multi-output Gaussian processes (MOGP), to capture both the correlation between vertices and the correlation between associated data. The proposed formulation is built on the definition of MOGP. This allows it to be applied to a wide range of data configurations and scenarios. Moreover, it has high expressive capability due to its flexibility in kernel design. It includes existing methods of Gaussian processes for graph-structured data as special cases and is possible to remove restrictions on data configurations, model selection, and inference scenarios in the existing methods. The performance of extensions achievable by the proposed formulation is evaluated through computer experiments with synthetic and real data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 22 May 2025.

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References · 14

  1. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  2. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246
  3. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 206
  4. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 132
  5. 2015Gaussian Processes for Data-Efficient Learning in Robotics and Controlcited by 98
  6. 2020Graph signal processing for machine learning: A review and new perspectivescited by 73
  7. 2020Mat'ern Gaussian Processes on Graphscited by 66
  8. 2020Gaussian Processes on Graphs via Spectral Kernel Learningcited by 24
  9. 2017A Time-Vertex Signal Processing Frameworkcited by 20
  10. 2018Gaussian Processes Over Graphscited by 14
  11. 2014Learning parametric dictionaries for graph signalscited by 12
  12. 2013Gaussian Process Optimization with Mutual Informationcited by 11
2 more
  1. 2024DGP-LVM: Derivative Gaussian process latent variable modelscited by 2
  2. 2022Transductive Kernels for Gaussian Processes on Graphscited by 1

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