  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.
