Chickenpox Cases in Hungary: A Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks

Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing. Newly proposed graph neural network architectures are repetitively evaluated on standard tasks such as traffic or weather forecasting. In this paper, we propose the Chickenpox Cases in Hungary dataset as a new dataset for comparing graph neural network architectures. Our time series analysis and forecasting experiments demonstrate that the Chickenpox Cases in Hungary dataset is adequate for comparing the predictive performance and forecasting capabilities of novel recurrent graph neural network architectures.
Original languageEnglish
Number of pages4
Publication statusPublished - 16 Apr 2021
EventWorkshop on Graph Learning Benchmarks @TheWebConf 2021 - Online
Duration: 16 Apr 202116 Apr 2021
https://graph-learning-benchmarks.github.io/

Conference

ConferenceWorkshop on Graph Learning Benchmarks @TheWebConf 2021
Abbreviated titleGLB 2021
Period16/04/2116/04/21
Internet address

Keywords / Materials (for Non-textual outputs)

  • cs.LG
  • cs.AI

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