Evolving-Graph Gaussian Processes

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openAccess

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Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2021-07

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Mcode

Degree programme

Language

en

Pages

6

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Abstract

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.

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Keywords

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

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Citation

Blanco-Mulero, D, Heinonen, M & Kyrki, V 2021, ' Evolving-Graph Gaussian Processes ', Paper presented at International Conference on Machine Learning: Time Series Workshop, Virtual, Online, 24/07/2021 - 24/07/2021 . < https://arxiv.org/abs/2106.15127 >