Evolving-Graph Gaussian Processes

David Blanco-Mulero*, Markus Heinonen, Ville Kyrki

*Corresponding author for this work

Research output: Contribution to conferencePaperScientificpeer-review

41 Downloads (Pure)


Graph Gaussian Processes (GGPs) provide a dataefficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data represent a dynamic structure, limiting the applications of GGPs. To overcome this we propose evolvingGraph Gaussian Processes (e-GGPs). The proposed method is capable of learning the transition function of graph vertices over time with a neighbourhood kernel to model the connectivity and interaction changes between vertices. We assess
the performance of our method on time-series regression problems where graphs evolve over time. We demonstrate the benefits of e-GGPs over static graph Gaussian Process approaches.
Original languageEnglish
Number of pages6
Publication statusPublished - Jul 2021
MoE publication typeNot Eligible
EventInternational Conference on Machine Learning: Time Series Workshop - Virtual, Online
Duration: 24 Jul 202124 Jul 2021


WorkshopInternational Conference on Machine Learning: Time Series Workshop
Abbreviated titleTSW-ICML
CityVirtual, Online
Internet address


  • gaussian process
  • Time series
  • Graph-based learning
  • probabilistic models


Dive into the research topics of 'Evolving-Graph Gaussian Processes'. Together they form a unique fingerprint.

Cite this