Evolving-Graph Gaussian Processes
Abstract
Graph Gaussian Processes (GGPs) provide a data-efficient 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 evolving-Graph 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.
Cite
@article{arxiv.2106.15127,
title = {Evolving-Graph Gaussian Processes},
author = {David Blanco-Mulero and Markus Heinonen and Ville Kyrki},
journal= {arXiv preprint arXiv:2106.15127},
year = {2021}
}
Comments
12 pages, 5 figures. Accepted for publication at ICML 2021 Time Series Workshop (TSW)